Ricardo Vinuesa publications per year

The chart shows the history of publications by Ricardo Vinuesa between 2010 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Ricardo Vinuesa published across 16 years, from 2010 to 2025, averaging 27.8 papers a year. Output peaked at 71 publications in 2024. 124 of the 444 publications appeared in the last two years.

No. of publications

20 40 60

2010 2025

444 publications in total across all disciplines

View publications per year as a table

Ricardo Vinuesa: publications per year, 2010 to 2025
Year	Publications
2010	1
2011	2
2012	1
2013	4
2014	6
2015	11
2016	16
2017	22
2018	19
2019	25
2020	40
2021	65
2022	48
2023	60
2024	71
2025	53
Total	444

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Ricardo Vinuesa publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Ricardo Vinuesa sits on this spectrum.

No. of scientists

50 100 150

47–56 publications 659+

This scientist: 328 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

View publications distribution as a table

Number of Mechanical and Aerospace Engineering scientists by publication count, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
Publications	Scientists	This scientist
47–56	10
57–66	23
67–76	32
77–86	62
87–96	67
97–106	91
107–116	113
117–126	115
127–136	130
137–146	140
147–156	155
157–166	132
167–176	133
177–186	130
187–196	140
197–206	115
207–216	125
217–226	117
227–236	99
237–246	92
247–256	100
257–266	95
267–276	88
277–286	77
287–296	74
297–306	74
307–316	62
317–326	70
327–336	59	328
337–346	58
347–356	45
357–366	44
367–376	36
377–386	41
387–396	32
397–406	23
407–416	28
417–426	27
427–436	25
437–446	23
447–456	23
457–466	20
467–476	12
477–486	24
487–496	18
497–506	12
507–516	13
517–526	21
527–536	12
537–546	8
547–556	16
557–566	3
567–576	11
577–586	6
587–596	5
597–606	6
607–616	7
617–626	7
627–636	10
637–646	4
647–656	3
657–658	2
659+	100

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Ricardo Vinuesa D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Ricardo Vinuesa sits on this spectrum.

No. of scientists

50 100 150

30 D-Index 93+

This scientist: 45 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

View D-Index distribution as a table

Number of Mechanical and Aerospace Engineering scientists by D-index, Research.com 2026 ranking edition. Based on 3,445 ranked scientists.
D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

Download as CSV

Best Publications

The role of artificial intelligence in achieving the Sustainable Development Goals

Ricardo Vinuesa;Hossein Azizpour;Iolanda Leite;Madeline Balaam

2723
Citations
Enhancing computational fluid dynamics with machine learning

Unknown

592
Citations
Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations.

Hamidreza Eivazi;Mojtaba Tahani;Philipp Schlatter;Ricardo Vinuesa

367
Citations
Predictions of turbulent shear flows using deep neural networks

P. A. Srinivasan;P. A. Srinivasan;L. Guastoni;Hossein Azizpour;Hossein Azizpour;Philipp Schlatter

275
Citations
History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers

Alexandra Bobke;Ricardo Vinuesa;Ramis Örlü;Philipp Schlatter

198
Citations
Convolutional-network models to predict wall-bounded turbulence from wall quantities

Luca Guastoni;Alejandro Güemes;Andrea Ianiro;Stefano Discetti

185
Citations
Turbulent boundary layers around wing sections up to Rec=1,000,000

Ricardo Vinuesa;Prabal Singh Negi;M. Atzori;Ardeshir Hanifi

159
Citations
Aspect ratio effects in turbulent duct flows studied through direct numerical simulation

Ricardo Vinuesa;Azad Noorani;Adrián Lozano-Durán;George K. El Khoury

158
Citations
Direct numerical simulation of the flow around a wing section at moderate Reynolds number

Seyed M. Hosseini;Ricardo Vinuesa;Philipp Schlatter;Ardeshir Hanifi;Ardeshir Hanifi

157
Citations
Improving aircraft performance using machine learning: A review

Unknown

147
Citations
On determining characteristic length scales in pressure-gradient turbulent boundary layers

Ricardo Vinuesa;Alexandra Bobke;Ramis Örlü;Philipp Schlatter

124
Citations
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows

Unknown

124
Citations
Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions

Unknown

123
Citations
Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows.

Unknown

122
Citations
An interpretable framework of data-driven turbulence modeling using deep neural networks

Unknown

121
Citations
Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using Pitot tubes

S C C Bailey;Marcus Hultmark;Jason Patrick Monty;P H Alfredsson

118
Citations
Deep reinforcement learning for turbulent drag reduction in channel flows

Unknown

118
Citations
The transformative potential of machine learning for experiments in fluid mechanics

Unknown

111
Citations
Convergence of numerical simulations of turbulent wall-bounded flows and mean cross-flow structure of rectangular ducts

Ricardo Vinuesa;Cezary Prus;Philipp Schlatter;Hassan M. Nagib

107
Citations
Effect of adverse pressure gradients on turbulent wing boundary layers

Alvaro Tanarro;Ricardo Vinuesa;Philipp Schlatter

96
Citations

Frequent Co-Authors

Philipp Schlatter Royal Institute of Technology

Ramis Örlü OsloMet – Oslo Metropolitan University

Hassan M. Nagib Illinois Institute of Technology

Dan S. Henningson Royal Institute of Technology

André V. G. Cavalieri Instituto Tecnológico de Aeronáutica

Koji Fukagata Keio University

Luca Brandt Polytechnic University of Turin

Wim Naudé RWTH Aachen University

Virginia Dignum Umeå University

Max Tegmark MIT

External Links

Personal website of Ricardo Vinuesa Google Scholar page

If you think any of the details on this page are incorrect, let us know.

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D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100

Ricardo Vinuesa

D-Index & Metrics

Ricardo Vinuesa publications per year

Ricardo Vinuesa publication distribution in Mechanical and Aerospace Engineering in 2026

Ricardo Vinuesa D-index placement in Mechanical and Aerospace Engineering in 2026

Best Publications

The role of artificial intelligence in achieving the Sustainable Development Goals

Enhancing computational fluid dynamics with machine learning

Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations.

Predictions of turbulent shear flows using deep neural networks

History effects and near equilibrium in adverse-pressure-gradient turbulent boundary layers

Turbulent boundary layers around wing sections up to Rec=1,000,000

Aspect ratio effects in turbulent duct flows studied through direct numerical simulation

Direct numerical simulation of the flow around a wing section at moderate Reynolds number

Improving aircraft performance using machine learning: A review

On determining characteristic length scales in pressure-gradient turbulent boundary layers

β-Variational autoencoders and transformers for reduced-order modelling of fluid flows

Recent advances in applying deep reinforcement learning for flow control: Perspectives and future directions

Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows.

An interpretable framework of data-driven turbulence modeling using deep neural networks

Obtaining accurate mean velocity measurements in high Reynolds number turbulent boundary layers using Pitot tubes

Deep reinforcement learning for turbulent drag reduction in channel flows

The transformative potential of machine learning for experiments in fluid mechanics

Convergence of numerical simulations of turbulent wall-bounded flows and mean cross-flow structure of rectangular ducts

Effect of adverse pressure gradients on turbulent wing boundary layers

Frequent Co-Authors

External Links

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Best Scientists Citing Ricardo Vinuesa

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D-Index	Scientists	This scientist
30	83
31	113
32	144
33	153
34	189
35	158
36	139
37	127
38	130
39	126
40	104
41	100
42	107
43	101
44	103
45	79	45
46	88
47	70
48	83
49	44
50	64
51	56
52	50
53	48
54	58
55	52
56	48
57	42
58	34
59	42
60	37
61	42
62	44
63	22
64	33
65	29
66	23
67	29
68	24
69	19
70	34
71	26
72	19
73	18
74	19
75	14
76	19
77	8
78	18
79	16
80	12
81	17
82	11
83	16
84	7
85	9
86	8
87	6
88	6
89	7
90	10
91	4
92	4
93+	100