Benjamin Sanderse

1.3k total citations · 1 hit paper
62 papers, 904 citations indexed

About

Benjamin Sanderse is a scholar working on Computational Mechanics, Statistical and Nonlinear Physics and Aerospace Engineering. According to data from OpenAlex, Benjamin Sanderse has authored 62 papers receiving a total of 904 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Computational Mechanics, 16 papers in Statistical and Nonlinear Physics and 16 papers in Aerospace Engineering. Recurrent topics in Benjamin Sanderse's work include Computational Fluid Dynamics and Aerodynamics (17 papers), Fluid Dynamics and Vibration Analysis (16 papers) and Model Reduction and Neural Networks (15 papers). Benjamin Sanderse is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (17 papers), Fluid Dynamics and Vibration Analysis (16 papers) and Model Reduction and Neural Networks (15 papers). Benjamin Sanderse collaborates with scholars based in Netherlands, United States and Norway. Benjamin Sanderse's co-authors include Barry Koren, S.P. van der Pijl, Jan‐Willem van Wingerden, A.E.P. Veldman, R.A.W.M. Henkes, Romit Maulik, Shady E. Ahmed, Daan Crommelin, Wouter Edeling and Panos Stinis and has published in prestigious journals such as Journal of Computational Physics, Renewable Energy and Desalination.

In The Last Decade

Benjamin Sanderse

57 papers receiving 849 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.

Review of computational fluid dynamics for wind turbine wake aerodynamics

2011 445 citations Benjamin Sanderse, Barry Koren et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Benjamin Sanderse Netherlands	11	558	475	412	94	92	62	904
Esteban Ferrer Spain	25	431 0.8×	1.2k 2.6×	130 0.3×	77 0.8×	362 3.9×	104	1.5k
Meilin Yu United States	15	324 0.6×	462 1.0×	92 0.2×	67 0.7×	60 0.7×	61	664
Cheng Huang United States	15	376 0.7×	850 1.8×	249 0.6×	31 0.3×	256 2.8×	66	1.1k
Scott M. Murman United States	20	668 1.2×	1.2k 2.4×	100 0.2×	35 0.4×	107 1.2×	112	1.3k
Shreyas Ananthan United States	16	668 1.2×	449 0.9×	283 0.7×	156 1.7×	26 0.3×	59	988
Christophe Corre France	18	243 0.4×	705 1.5×	109 0.3×	36 0.4×	75 0.8×	52	912
Jeffrey P. Slotnick United States	17	644 1.2×	1.1k 2.2×	190 0.5×	26 0.3×	114 1.2×	28	1.3k
Boris Diskin United States	22	361 0.6×	1.4k 2.9×	92 0.2×	63 0.7×	130 1.4×	113	1.6k
P. Queutey France	17	365 0.7×	962 2.0×	381 0.9×	36 0.4×	27 0.3×	48	1.2k
Andrea Beck Germany	17	233 0.4×	1.1k 2.3×	137 0.3×	49 0.5×	386 4.2×	65	1.4k

Countries citing papers authored by Benjamin Sanderse

Since Specialization

Citations

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

Fields of papers citing papers by Benjamin Sanderse

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Benjamin Sanderse

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Sanderse. A scholar is included among the top collaborators of Benjamin Sanderse 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 Benjamin Sanderse. Benjamin Sanderse 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

Sanderse, Benjamin, et al.. (2025). Entropy-stable model reduction of one-dimensional hyperbolic systems using rational quadratic manifolds. Journal of Computational Physics. 528. 113817–113817. 1 indexed citations

Sanderse, Benjamin, et al.. (2024). A pressure-free long-time stable reduced-order model for two-dimensional Rayleigh–Bénard convection. Chaos An Interdisciplinary Journal of Nonlinear Science. 34(2). 2 indexed citations

Sanderse, Benjamin, et al.. (2024). Scientific Machine Learning: A Symbiosis. 7(1). i–x.

Sanderse, Benjamin, et al.. (2024). Dynamic wind farm flow control using free-vortex wake models. Wind energy science. 9(3). 721–740. 5 indexed citations

Sanderse, Benjamin, et al.. (2024). Energy-stable discretization of the one-dimensional two-fluid model. International Journal of Multiphase Flow. 174. 104756–104756. 1 indexed citations

Sanderse, Benjamin, et al.. (2024). A new temperature evolution equation that enforces thermodynamic vapour–liquid equilibrium in multiphase flows - application to CO2 modelling. Computers & Fluids. 289. 106524–106524.

Edeling, Wouter, et al.. (2024). Energy-conserving neural network for turbulence closure modeling. Journal of Computational Physics. 508. 113003–113003. 9 indexed citations

Sanderse, Benjamin. (2023). G. Rozza, G. Stabile, F. Ballarin: “Advanced Reduced Order Methods and Applications in Computational Fluid Dynamics”. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 125(3). 191–194. 1 indexed citations

Tavernier, Delphine De, et al.. (2023). Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects. Wind energy science. 8(12). 1909–1925. 5 indexed citations

10.

Crommelin, Daan, et al.. (2023). Comparison of neural closure models for discretised PDEs. Computers & Mathematics with Applications. 143. 94–107. 12 indexed citations

11.

Sanderse, Benjamin, et al.. (2022). Energy‐consistent formulation of the pressure‐free two‐fluid model. International Journal for Numerical Methods in Fluids. 95(5). 869–898. 2 indexed citations

12.

Sanderse, Benjamin, et al.. (2022). Energy-conserving formulation of the two-fluid model for incompressible two-phase flow in channels and pipes. Computers & Fluids. 244. 105533–105533. 4 indexed citations

13.

Sanderse, Benjamin, et al.. (2022). Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling. Wind energy science. 7(2). 759–781. 8 indexed citations

14.

Coffeng, Luc E., et al.. (2021). Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model. PLoS Computational Biology. 17(9). e1009355–e1009355. 14 indexed citations

15.

Sanderse, Benjamin, et al.. (2021). Efficient Bayesian calibration of aerodynamic wind turbine models using surrogate modeling. Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands. 1 indexed citations

16.

Sanderse, Benjamin, et al.. (2020). Reduced order models for the incompressible Navier-Stokes equations on\n collocated grids using a 'discretize-then-project' approach. arXiv (Cornell University). 7 indexed citations

17.

Rodríguez, Cristian, et al.. (2020). Assessment of sensitivity and accuracy of BEM-based aeroelastic models on wind turbine load predictions. Journal of Physics Conference Series. 1618(4). 42015–42015. 1 indexed citations

18.

Weippl, Edgar & Benjamin Sanderse. (2018). Digital Twins - Introduction to the Special Theme.. ERCIM news/ERCIM news online edition. 2018. 4 indexed citations

19.

Sanderse, Benjamin, et al.. (2015). Efficient simulation of one-dimensional two-phase flow with a new high-order Discontinuous Galerkin method. Data Archiving and Networked Services (DANS). 1 indexed citations

20.

Sanderse, Benjamin & Barry Koren. (2012). New explicit Runge-Kutta methods for the incompressible Navier-Stokes equations. TU/e Research Portal. 173(3). 439–439. 1 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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