Dmitry Alexeev

PhD Student

Research and Interests

Blood flow simulations
Particle methods
HPC and GPU computing

Education

M.S. Applied Mathematics and Computer Science, Lomonosov Moscow State University, 2012

2017

G. Novati, S. Verma, D. Alexeev, D. Rossinelli, W. M. van Rees, and P. Koumoutsakos, “Synchronisation through learning for two self-propelled swimmers,” Bioinspir. Biomim., vol. 12, iss. 3, p. 36001, 2017.
[BibTeX] [PDF] [DOI]

@article{novati2017a,
author = {Guido Novati and Siddhartha Verma and Dmitry Alexeev and Diego Rossinelli and Wim M van Rees and Petros Koumoutsakos},
doi = {10.1088/1748-3190/aa6311},
journal = {{Bioinspir. Biomim.}},
month = {mar},
number = {3},
pages = {036001},
publisher = {{IOP} Publishing},
title = {Synchronisation through learning for two self-propelled swimmers},
url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/novati2017a.pdf},
volume = {12},
year = {2017}
}

2016

M. Gazzola, A. A. Tchieu, D. Alexeev, A. de Brauer, and P. Koumoutsakos, “Learning to school in the presence of hydrodynamic interactions,” J. Fluid Mech., vol. 789, p. 726–749, 2016.
[BibTeX] [PDF] [DOI]

@article{gazzola2016a,
author = {M. Gazzola and A. A. Tchieu and D. Alexeev and A. de Brauer and P. Koumoutsakos},
doi = {10.1017/jfm.2015.686},
journal = {{J. Fluid Mech.}},
month = {jan},
pages = {726--749},
publisher = {Cambridge University Press ({CUP})},
title = {Learning to school in the presence of hydrodynamic interactions},
url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/gazzola2016a.pdf},
volume = {789},
year = {2016}
}

2015

D. Alexeev, J. Chen, J. H. Walther, K. P. Giapis, P. Angelikopoulos, and P. Koumoutsakos, “Kapitza resistance between few-layer graphene and water: liquid layering effects,” Nano Lett., vol. 15, iss. 9, p. 5744–5749, 2015.
[BibTeX] [Abstract] [Supplemental] [PDF] [DOI]

The Kapitza resistance (R_K) between few-layer graphene (FLG) and water was studied using molecular dynamics simulations. The R_K was found to depend on the number of the layers in the FLG though, surprisingly, not on the water block thickness. This distinct size dependence is attributed to the large difference in the phonon mean free path between the FLG and water. Remarkably, R_K is strongly dependent on the layering of water adjacent to the FLG, exhibiting an inverse proportionality relationship to the peak density of the first water layer, which is consistent with better acoustic phonon matching between FLG and water. These findings suggest novel ways to engineer the thermal transport properties of solid{–}liquid interfaces by controlling and regulating the liquid layering at the interface.
```
@article{alexeev2015a,
author = {Dmitry Alexeev and Jie Chen and Jens H. Walther and Konstantinos P. Giapis and Panagiotis Angelikopoulos and Petros Koumoutsakos},
doi = {10.1021/acs.nanolett.5b03024},
journal = {{Nano Lett.}},
month = {sep},
number = {9},
pages = {5744--5749},
publisher = {American Chemical Society ({ACS})},
supplemental = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/alexeev2015a_supplemental.pdf},
title = {Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering Effects},
url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/alexeev2015a.pdf},
volume = {15},
year = {2015}
}
```

C. Conti, D. Rossinelli, D. Alexeev, K. Lykov, P. Hadjidoukas, and P. Koumoutsakos, “Video: the in-silico lab-on-a-chip – catching a needle in a flowing haystack,” in 68th annual meeting of the APS division of fluid dynamics – gallery of fluid motion, 2015.
[BibTeX] [Movie] [DOI]

@inproceedings{conti2015a,
author = {Christian Conti and Diego Rossinelli and Dmitry Alexeev and Kirill Lykov and Panagiotis Hadjidoukas and Petros Koumoutsakos},
booktitle = {68th Annual Meeting of the {APS} Division of Fluid Dynamics - Gallery of Fluid Motion},
doi = {10.1103/aps.dfd.2015.gfm.v0008},
month = {nov},
movie = {https://www.youtube.com/watch?v=nVKjxqIP6I4},
publisher = {American Physical Society},
title = {Video: The In-Silico Lab-on-a-Chip - Catching a Needle in a Flowing Haystack},
year = {2015}
}

D. Rossinelli, Y. Tang, K. Lykov, D. Alexeev, M. Bernaschi, P. Hadjidoukas, M. Bisson, W. Joubert, C. Conti, G. Karniadakis, M. Fatica, I. Pivkin, and P. Koumoutsakos, “The In-Silico Lab-on-a-Chip: petascale and high-throughput simulations of microfluidics at cell resolution,” in Proceedings of the international conference for high performance computing, networking, storage and analysis – SC ’15, 2015.
[BibTeX] [PDF] [DOI]

@inproceedings{rossinelli2015b,
author = {Diego Rossinelli and Yu-Hang Tang and Kirill Lykov and Dmitry Alexeev and Massimo Bernaschi and Panagiotis Hadjidoukas and Mauro Bisson and Wayne Joubert and Christian Conti and George Karniadakis and Massimiliano Fatica and Igor Pivkin and Petros Koumoutsakos},
booktitle = {Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis – {SC} {\textquotesingle}15},
doi = {10.1145/2807591.2807677},
number = {Article 2},
publisher = {{ACM} Press},
title = {The {In-Silico Lab-on-a-Chip}: Petascale and High-Throughput Simulations of Microfluidics at Cell Resolution},
url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/rossinelli2015b.pdf},
year = {2015}
}

2014

P. E. Hadjidoukas, P. Angelikopoulos, D. Rossinelli, D. Alexeev, C. Papadimitriou, and P. Koumoutsakos, “Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials,” Comput. Method. Appl. M., vol. 282, p. 218–238, 2014.
[BibTeX] [Abstract] [PDF] [DOI]

Predictions in the behavior of granular materials using Discrete Element Methods (DEM) hinge on the employed interaction potentials. Here we introduce a data driven, Bayesian framework to quantify DEM predictions. Our approach relies on experimentally measured coefficients of restitution for single steel particle{–}wall collisions. The calibration data entail both tangential and normal coefficients of restitution, for varying impact angles and speeds of the bouncing particle. The parametric uncertainty in multiple Force{–}Displacement models is estimated using an enhanced Transitional Markov Chain Monte Carlo implemented efficiently on parallel computer architectures. In turn, the parametric model uncertainties are propagated to predict Quantities of Interest (QoI) for two testbed applications: silo discharge and vibration induced mass-segregation. This work demonstrates that the classical way of calibrating DEM potentials, through parameter optimization, is insufficient and it fails to provide robust predictions. The present Bayesian framework provides robust predictions for the behavior of granular materials using DEM simulations. Most importantly the results demonstrate the importance of including parametric and modeling uncertainties in the potentials employed in Discrete Element Methods.
```
@article{hadjidoukas2014c,
author = {P.E. Hadjidoukas and P. Angelikopoulos and D. Rossinelli and D. Alexeev and C. Papadimitriou and P. Koumoutsakos},
doi = {10.1016/j.cma.2014.07.017},
journal = {{Comput. Method. Appl. M.}},
month = {dec},
pages = {218--238},
publisher = {Elsevier {BV}},
title = {{B}ayesian uncertainty quantification and propagation for discrete element simulations of granular materials},
url = {http://www.cse-lab.ethz.ch/wp-content/papercite-data/pdf/hadjidoukas2014c.pdf},
volume = {282},
year = {2014}
}
```

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