Tuhfe Göçmen

1.6k total citations · 1 hit paper
46 papers, 982 citations indexed

About

Tuhfe Göçmen is a scholar working on Aerospace Engineering, Electrical and Electronic Engineering and Environmental Engineering. According to data from OpenAlex, Tuhfe Göçmen has authored 46 papers receiving a total of 982 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Aerospace Engineering, 25 papers in Electrical and Electronic Engineering and 15 papers in Environmental Engineering. Recurrent topics in Tuhfe Göçmen's work include Wind Energy Research and Development (32 papers), Wind Turbine Control Systems (14 papers) and Wind and Air Flow Studies (13 papers). Tuhfe Göçmen is often cited by papers focused on Wind Energy Research and Development (32 papers), Wind Turbine Control Systems (14 papers) and Wind and Air Flow Studies (13 papers). Tuhfe Göçmen collaborates with scholars based in Denmark, Germany and United States. Tuhfe Göçmen's co-authors include Gregor Giebel, Gunner Chr. Larsen, Pierre‐Elouan Réthoré, Barış Özerdem, Søren Ott, P. van der Laan, Alfredo Peña, Søren Juhl Andersen, Johan Meyers and Carlo L. Bottasso and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Energy and Renewable Energy.

In The Last Decade

Tuhfe Göçmen

44 papers receiving 944 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.

Wind turbine wake models developed at the technical university of Denmark: A review

2016 256 citations Tuhfe Göçmen, P. van der Laan et al. Renewable and Sustainable Energy Reviews profile →

Peers

Tuhfe Göçmen

AE

EE

EEE

CM

AI

Christopher J. Bay United States

Haiying Sun Hong Kong

Jennifer King United States

Ahmad Vasel‐Be‐Hagh United States

Andrew Scholbrock United States

M. Thøgersen Denmark

Jennifer Annoni United States

Eric Simley United States

Katherine Dykes United States

Yaoran Chen China

Christopher J. Bay United States

Tuhfe Göçmen

926 ×1.2

AE

455 ×1.1

EE

452 ×1.1

EEE

285 ×1.2

CM

67 ×0.6

AI

Citations per year, relative to Tuhfe Göçmen Tuhfe Göçmen (= 1×) peers Christopher J. Bay

Countries citing papers authored by Tuhfe Göçmen

Since Specialization

Citations

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

Fields of papers citing papers by Tuhfe Göçmen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tuhfe Göçmen

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Göçmen, Tuhfe, Nikolay Dimitrov, Søren Juhl Andersen, et al.. (2025). Data-driven wind farm flow control and challenges towards field implementation: A review. Renewable and Sustainable Energy Reviews. 216. 115605–115605. 3 indexed citations

2.

Hasager, Charlotte Bay, et al.. (2025). Copula-based joint distributions of rain and wind for leading edge erosion risk atlas. Renewable Energy. 253. 123358–123358.

3.

Das, Kaushik, Anca Daniela Hansen, Juan Pablo Murcia León, et al.. (2025). Research Challenges and Opportunities of Utility‐Scale Hybrid Power Plants. Wiley Interdisciplinary Reviews Energy and Environment. 14(1). 3 indexed citations

4.

Madsen, Anja Lykke, et al.. (2025). Uncertainty of toggling in wake steering experiments under diurnal cycle atmospheric conditions: an LES study. Journal of Physics Conference Series. 3016(1). 12026–12026. 1 indexed citations

5.

Göçmen, Tuhfe, et al.. (2024). Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling. Journal of Physics Conference Series. 2767(9). 92039–92039. 1 indexed citations

6.

Göçmen, Tuhfe, et al.. (2024). Erosion-safe operation using double deep Q-learning. Journal of Physics Conference Series. 2767(3). 32047–32047. 3 indexed citations

7.

Hulsman, Petra, Maryhope Howland, Tuhfe Göçmen, & Vlaho Petrović‬. (2024). Assessing Closed-Loop Data-Driven Wind Farm Control Strategies within a Wind Tunnel. Journal of Physics Conference Series. 2767(3). 32049–32049. 2 indexed citations

8.

Göçmen, Tuhfe, et al.. (2023). Model‐free closed‐loop wind farm control using reinforcement learning with recursive least squares. Wind Energy. 27(11). 1173–1187. 10 indexed citations

9.

Göçmen, Tuhfe, et al.. (2023). Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations. Wind energy science. 8(2). 173–191. 12 indexed citations

10.

Göçmen, Tuhfe, et al.. (2023). Efficient Mann turbulence generation for offshore wind farms with applications in fatigue load surrogate modelling. Journal of Physics Conference Series. 2626(1). 12050–12050. 4 indexed citations

11.

Kölle, Konstanze, Tuhfe Göçmen, Paula B. Garcia‐Rosa, et al.. (2022). Towards integrated wind farm control: Interfacing farm flow and power plant controls. 4(2). 6 indexed citations

12.

Dimitrov, Nikolay & Tuhfe Göçmen. (2022). Virtual sensors for wind turbines with machine learning‐based time series models. Wind Energy. 25(9). 1626–1645. 23 indexed citations

13.

Meyers, Johan, Carlo L. Bottasso, Katherine Dykes, et al.. (2022). Wind farm flow control: prospects and challenges. Wind energy science. 7(6). 2271–2306. 120 indexed citations

14.

Göçmen, Tuhfe, Paula B. Garcia‐Rosa, Kaushik Das, et al.. (2021). Wind farm flow control oriented to electricity markets and grid integration: Initial perspective analysis. 3(3). 17 indexed citations

15.

Göçmen, Tuhfe, et al.. (2021). Applied Machine Learning Techniques for Performance Analysis in Large Wind Farms. Energies. 14(13). 3756–3756. 8 indexed citations

16.

Dykes, Katherine, et al.. (2021). Technology in context: optimization of wind farm and hybrid power plant design, operation and control. DTU Data. 1 indexed citations

17.

Andersen, Søren Juhl, et al.. (2020). Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations. Wind energy science. 5(1). 309–329. 39 indexed citations

18.

Girard, Nicolas, et al.. (2019). Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm. Wind energy science. 4(2). 287–302. 32 indexed citations

19.

Göçmen, Tuhfe & Gregor Giebel. (2016). Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms. Renewable Energy. 99. 524–532. 51 indexed citations

20.

Göçmen, Tuhfe, P. van der Laan, Pierre‐Elouan Réthoré, et al.. (2016). Wind turbine wake models developed at the technical university of Denmark: A review. Renewable and Sustainable Energy Reviews. 60. 752–769. 256 indexed citations breakdown →

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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