D. L. Youngs

6.0k total citations · 2 hit papers
55 papers, 4.5k citations indexed

About

D. L. Youngs is a scholar working on Computational Mechanics, Nuclear and High Energy Physics and Ocean Engineering. According to data from OpenAlex, D. L. Youngs has authored 55 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Computational Mechanics, 39 papers in Nuclear and High Energy Physics and 13 papers in Ocean Engineering. Recurrent topics in D. L. Youngs's work include Laser-Plasma Interactions and Diagnostics (39 papers), Fluid Dynamics and Turbulent Flows (37 papers) and Computational Fluid Dynamics and Aerodynamics (25 papers). D. L. Youngs is often cited by papers focused on Laser-Plasma Interactions and Diagnostics (39 papers), Fluid Dynamics and Turbulent Flows (37 papers) and Computational Fluid Dynamics and Aerodynamics (25 papers). D. L. Youngs collaborates with scholars based in United Kingdom, United States and Australia. D. L. Youngs's co-authors include Dimitris Drikakis, R. J. R. Williams, Ben Thornber, P. F. Linden, Stuart B. Dalziel, J. M. Redondo, Ioannis W. Kokkinakis, Andrew Mosedale, Fernando F. Grinstein and Christer Fureby and has published in prestigious journals such as Journal of Fluid Mechanics, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

D. L. Youngs

54 papers receiving 4.3k citations

Hit Papers

Time-Dependent Multi-Material Flow with Large Fluid Disto... 1982 2026 1996 2011 1982 1984 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
D. L. Youngs United Kingdom 28 3.7k 1.8k 830 580 332 55 4.5k
Paul E. Dimotakis United States 39 5.6k 1.5× 416 0.2× 793 1.0× 2.8k 4.8× 269 0.8× 145 6.7k
G. Ben‐Dor Israel 38 3.1k 0.9× 923 0.5× 562 0.7× 1.8k 3.1× 1.1k 3.3× 293 5.5k
Fernando F. Grinstein United States 31 4.3k 1.2× 496 0.3× 395 0.5× 2.2k 3.8× 154 0.5× 134 5.0k
B. Sturtevant United States 22 1.2k 0.3× 635 0.4× 249 0.3× 456 0.8× 157 0.5× 50 2.1k
K. Takayama Japan 28 1.4k 0.4× 479 0.3× 380 0.5× 946 1.6× 208 0.6× 159 2.4k
Andrew W. Cook United States 23 2.2k 0.6× 793 0.4× 418 0.5× 345 0.6× 146 0.4× 51 2.7k
Takashi Yabe Japan 27 2.1k 0.6× 193 0.1× 222 0.3× 455 0.8× 304 0.9× 98 3.6k
Ben Thornber Australia 22 1.8k 0.5× 809 0.5× 308 0.4× 753 1.3× 81 0.2× 89 2.2k
H. G. Hornung United States 34 2.9k 0.8× 251 0.1× 289 0.3× 1.8k 3.1× 236 0.7× 156 3.8k
Rémi Abgrall France 36 6.1k 1.7× 256 0.1× 258 0.3× 1.0k 1.8× 446 1.3× 175 6.9k

Countries citing papers authored by D. L. Youngs

Since Specialization
Citations

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

Fields of papers citing papers by D. L. Youngs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of D. L. Youngs

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

All Works

20 of 20 papers shown
1.
Groom, Michael, et al.. (2024). Impact of axial strain on linear, transitional and self-similar turbulent mixing layers. Journal of Fluid Mechanics. 999. 1 indexed citations
2.
Youngs, D. L. & Ben Thornber. (2020). Early Time Modifications to the Buoyancy-Drag Model for Richtmyer–Meshkov Mixing. Journal of Fluids Engineering. 142(12). 8 indexed citations
3.
Youngs, D. L., et al.. (2019). Three-dimensional simulations of turbulent mixing in spherical implosions. Physics of Fluids. 31(11). 21 indexed citations
4.
Thornber, Ben, Michael Groom, & D. L. Youngs. (2018). A five-equation model for the simulation of miscible and viscous compressible fluids. Journal of Computational Physics. 372. 256–280. 21 indexed citations
5.
Clark, D. S., et al.. (2018). Single-mode perturbation growth in an idealized spherical implosion. Journal of Computational Physics. 371. 801–819. 17 indexed citations
6.
Youngs, D. L.. (2017). Rayleigh–Taylor mixing: direct numerical simulation and implicit large eddy simulation. Physica Scripta. 92(7). 74006–74006. 47 indexed citations
7.
Kokkinakis, Ioannis W., Dimitris Drikakis, D. L. Youngs, & R. J. R. Williams. (2015). Two-equation and multi-fluid turbulence models for Rayleigh–Taylor mixing. International Journal of Heat and Fluid Flow. 56. 233–250. 45 indexed citations
8.
Andrews, Malcolm, D. L. Youngs, Daniel Livescu, & Tie Wei. (2014). Computational Studies of Two-Dimensional Rayleigh-Taylor Driven Mixing for a Tilted-Rig. Journal of Fluids Engineering. 136(9). 28 indexed citations
10.
Thornber, Ben, Dimitris Drikakis, D. L. Youngs, & R. J. R. Williams. (2012). Physics of the single-shocked and reshocked Richtmyer–Meshkov instability. Journal of Turbulence. 13. N10–N10. 101 indexed citations
11.
Youngs, D. L.. (2010). Large Eddy Simulation and 1D/2D Engineering Models for Rayleigh-Taylor Mixing. 2 indexed citations
12.
Thornber, Ben, Dimitris Drikakis, D. L. Youngs, & R. J. R. Williams. (2010). The influence of initial conditions on turbulent mixing due to Richtmyer–Meshkov instability. Journal of Fluid Mechanics. 654. 99–139. 164 indexed citations
13.
Youngs, D. L.. (2009). Application of monotone integrated large eddy simulation to Rayleigh–Taylor mixing. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 367(1899). 2971–2983. 30 indexed citations
14.
Youngs, D. L., et al.. (2009). Rayleigh-Taylor instability and mixing. Scholarpedia. 4(2). 6092–6092. 7 indexed citations
15.
Youngs, D. L.. (2008). Turbulent Mixing due to Rayleigh-Taylor Instability. Bulletin of the American Physical Society. 61. 2 indexed citations
16.
Thornber, Ben, Andrew Mosedale, Dimitris Drikakis, D. L. Youngs, & R. J. R. Williams. (2008). An improved reconstruction method for compressible flows with low Mach number features. Journal of Computational Physics. 227(10). 4873–4894. 227 indexed citations
17.
Linden, P. F., J. M. Redondo, & D. L. Youngs. (1994). Molecular mixing in Rayleigh–Taylor instability. Journal of Fluid Mechanics. 265. 97–124. 137 indexed citations
18.
Youngs, D. L.. (1992). Rayleigh-Taylor instability: numerical simulation and experiment. Plasma Physics and Controlled Fusion. 34(13). 2071–2076. 5 indexed citations
19.
Youngs, D. L.. (1991). Three-dimensional numerical simulation of turbulent mixing by Rayleigh–Taylor instability. Physics of Fluids A Fluid Dynamics. 3(5). 1312–1320. 259 indexed citations
20.
Youngs, D. L.. (1984). Numerical simulation of turbulent mixing by Rayleigh-Taylor instability. Physica D Nonlinear Phenomena. 12(1-3). 32–44. 509 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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