Thomas D. Dunstan

630 total citations
26 papers, 473 citations indexed

About

Thomas D. Dunstan is a scholar working on Computational Mechanics, Environmental Engineering and Fluid Flow and Transfer Processes. According to data from OpenAlex, Thomas D. Dunstan has authored 26 papers receiving a total of 473 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computational Mechanics, 11 papers in Environmental Engineering and 11 papers in Fluid Flow and Transfer Processes. Recurrent topics in Thomas D. Dunstan's work include Combustion and flame dynamics (14 papers), Advanced Combustion Engine Technologies (11 papers) and Wind and Air Flow Studies (10 papers). Thomas D. Dunstan is often cited by papers focused on Combustion and flame dynamics (14 papers), Advanced Combustion Engine Technologies (11 papers) and Wind and Air Flow Studies (10 papers). Thomas D. Dunstan collaborates with scholars based in United Kingdom, United States and Denmark. Thomas D. Dunstan's co-authors include N. Swaminathan, Yuki Minamoto, R.S. Cant, Nilanjan Chakraborty, Takafumi Nishino, Karl W. Jenkins, K. N. C. Bray, K.N.C. Bray, N. Kingsbury and Naoya Fukushima and has published in prestigious journals such as Journal of Fluid Mechanics, International Journal of Hydrogen Energy and Combustion and Flame.

In The Last Decade

Thomas D. Dunstan

26 papers receiving 470 citations

Peers

Thomas D. Dunstan

FFTP

SRRQ

Thomas Jaravel France

Dennis P. Stocker United States

Jeffrey W. Labahn Canada

Mark Sweeney United Kingdom

Dominik Ebi Switzerland

Robert Giezendanner-Thoben Germany

Felix Guethe Switzerland

T. Mantel United States

Jim Rogerson United Kingdom

J. M. Samaniego France

Thomas Jaravel France

Thomas D. Dunstan

357 ×0.9

256 ×0.8

FFTP

116 ×0.6

SRRQ

137 ×1.1

47 ×0.5

Citations per year, relative to Thomas D. Dunstan Thomas D. Dunstan (= 1×) peers Thomas Jaravel

Countries citing papers authored by Thomas D. Dunstan

Since Specialization

Citations

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

Fields of papers citing papers by Thomas D. Dunstan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas D. Dunstan. 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 Thomas D. Dunstan. The network helps show where Thomas D. Dunstan may publish in the future.

Co-authorship network of co-authors of Thomas D. Dunstan

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas D. Dunstan. A scholar is included among the top collaborators of Thomas D. Dunstan 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 Thomas D. Dunstan. Thomas D. Dunstan 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

Nishino, Takafumi, et al.. (2025). Turbine- and farm-scale power losses in wind farms: an alternative to wake and farm blockage losses. Wind energy science. 10(2). 435–450. 1 indexed citations

Kent, Chris, Adam A. Scaife, Nick Dunstone, et al.. (2025). Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data. npj Climate and Atmospheric Science. 8(1). 1 indexed citations

Dunstan, Thomas D., et al.. (2023). An analytical model of momentum availability for predicting large wind farm power. Journal of Fluid Mechanics. 976. 6 indexed citations

Briol, François‐Xavier, et al.. (2023). Data‐driven modelling of turbine wake interactions and flow resistance in large wind farms. Wind Energy. 26(9). 968–984. 8 indexed citations

Hardiman, Steven C., Adam A. Scaife, Van Niekerk, et al.. (2023). Machine Learning for Nonorographic Gravity Waves in a Climate Model. 2(4). 6 indexed citations

Coney, J.E.R., Leif Denby, Andrew Ross, et al.. (2023). Identifying and characterising trapped lee waves using deep learning techniques. Quarterly Journal of the Royal Meteorological Society. 150(758). 213–231. 1 indexed citations

Nishino, Takafumi, et al.. (2022). Two-scale interaction of wake and blockage effects in large wind farms. Journal of Fluid Mechanics. 953. 14 indexed citations

Dunstan, Thomas D., et al.. (2021). Time-Dependent Upper Limits to the Performance of Large Wind Farms Due to Mesoscale Atmospheric Response. Energies. 14(19). 6437–6437. 10 indexed citations

Williams, K. D., Annelize van Niekerk, Martin Best, et al.. (2020). Addressing the causes of large‐scale circulation error in the Met Office Unified Model. Quarterly Journal of the Royal Meteorological Society. 146(731). 2597–2613. 12 indexed citations

10.

Dunstan, Thomas D., et al.. (2016). Empirical error correction and feature identification for long term wind resource assessment using support vector regression. Journal of Renewable and Sustainable Energy. 8(1). 4 indexed citations

11.

Dunstan, Thomas D., et al.. (2016). Computation of Forced Premixed Flames Dynamics. Combustion Science and Technology. 188(7). 1115–1135. 12 indexed citations

12.

Gao, Yuan, Nilanjan Chakraborty, Thomas D. Dunstan, & N. Swaminathan. (2015). Assessment of Reynolds Averaged Navier–Stokes Modeling of Scalar Dissipation Rate Transport in Turbulent Oblique Premixed Flames. Combustion Science and Technology. 187(10). 1584–1609. 8 indexed citations

13.

Dunstan, Thomas D., N. Swaminathan, K. N. C. Bray, & N. Kingsbury. (2013). Flame Interactions in Turbulent Premixed Twin V-Flames. Combustion Science and Technology. 185(1). 134–159. 26 indexed citations

14.

Liu, Yu, Ann P. Dowling, Thomas D. Dunstan, & N. Swaminathan. (2012). Modelling of Combustion Noise Spectrum from Turbulent Premixed Flames. View. 2 indexed citations

15.

Dunstan, Thomas D., Yuki Minamoto, Nilanjan Chakraborty, & N. Swaminathan. (2012). Scalar dissipation rate modelling for Large Eddy Simulation of turbulent premixed flames. Proceedings of the Combustion Institute. 34(1). 1193–1201. 93 indexed citations

16.

Minamoto, Yuki, Thomas D. Dunstan, N. Swaminathan, & R.S. Cant. (2012). DNS of EGR-type turbulent flame in MILD condition. Proceedings of the Combustion Institute. 34(2). 3231–3238. 71 indexed citations

17.

Liu, Yu, Ann P. Dowling, N. Swaminathan, & Thomas D. Dunstan. (2012). Spatial correlation of heat release rate and sound emission from turbulent premixed flames. Combustion and Flame. 159(7). 2430–2440. 18 indexed citations

18.

Liu, Yu, et al.. (2011). Prediction of Noise Source for an Aeroengine Combustor. View. 6 indexed citations

19.

Minamoto, Yuki, Naoya Fukushima, Mamoru Tanahashi, et al.. (2011). Effect of flow-geometry on turbulence-scalar interaction in premixed flames. Physics of Fluids. 23(12). 34 indexed citations

20.

Dunstan, Thomas D., N. Swaminathan, K. N. C. Bray, & R.S. Cant. (2010). Geometrical Properties and Turbulent Flame Speed Measurements in Stationary Premixed V-flames Using Direct Numerical Simulation. Flow Turbulence and Combustion. 87(2-3). 237–259. 36 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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