Vladimir Palmin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Vladimir Palmin has a total of 27 co-authors. Here are the top co-authors by number of shared publications:
- Agnieszka Trzcinska
University of Warsaw
1 shared publication Alexander Nozik
Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
1 shared publication- Alexander Nozik
Moscow Institute of Physics and Technology
1 shared publication Alexander Perepukhov
Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
1 shared publication- Alexei Zinovev
Department of Educational Programmes, Institute of Education Faculty, HSE University, 101000 Moscow, Russia
1 shared publication Andrey Mukhin
Moscow Institute of Physics and Technology (National Research University) - MIPT, 1 "A" Kerchenskaya st., Moscow, 117303, Russia
1 shared publication
Latest Publications
- 27 June 2024
- Published by American Physical Society (APS) in Physical Review C
- Vol. 109 (6) , 064625
Dissipation by transfer and its influence on barrier distributions
The quasielastic barrier distribution () of the system exhibits a single broad structureless peak, unlike the results of coupled-channels calculations, including the strongest collective states of both projectile and target, which show a pronounced double-peaked structure. Separate measurements of the transfer strength, combined with the relatively small number of weak noncollective states in , suggested that the observed barrier smoothing in this system could be due to the influence of couplings to transfer reactions. The coupled reaction channels (CRC) calculations performed by explicitly coupling to the two strongest transfer partitions, one-neutron pickup and one-proton stripping, suggest that the cumulative effect of coupling to many relatively weak transfer channels is responsible for the barrier smoothing rather than a few strong transfer reactions. In this perspective, the four main transfer channels (one- and two-neutron pickup and one-proton and one-alpha stripping) of the system were coupled to the collective excitations by employing an upgraded ccqel code, where we introduced the dependence of the transfer coupling strength on the transferred particle and experimental -value distributions. Because of the availability of distributions measured at the two beam energies of 96 and 103 MeV, two sets of transfer coupling strength were determined for each transfer channel. The study with the upgraded code indicates transfers with negative ground-to-ground states value as the leading cause of the smearing of the barrier distribution. At the beam energy of 103 MeV, the smoothing of is influenced mainly by the one-neutron pickup. On the other hand, the one-neutron pickup and one-proton stripping transfers are dominant for the beam energy of 96 MeV. These results highlight the importance of the transfer coupling dependence on the experimental -value distribution and, consequently, on the dissipation of the projectile kinetic energy.- 27 June 2024
- Published by American Physical Society (APS) in Physical Review C
- Vol. 109 (6) , 064625
Dissipation by transfer and its influence on barrier distributions
The quasielastic barrier distribution () of the system exhibits a single broad structureless peak, unlike the results of coupled-channels calculations, including the strongest collective states of both projectile and target, which show a pronounced double-peaked structure. Separate measurements of the transfer strength, combined with the relatively small number of weak noncollective states in , suggested that the observed barrier smoothing in this system could be due to the influence of couplings to transfer reactions. The coupled reaction channels (CRC) calculations performed by explicitly coupling to the two strongest transfer partitions, one-neutron pickup and one-proton stripping, suggest that the cumulative effect of coupling to many relatively weak transfer channels is responsible for the barrier smoothing rather than a few strong transfer reactions. In this perspective, the four main transfer channels (one- and two-neutron pickup and one-proton and one-alpha stripping) of the system were coupled to the collective excitations by employing an upgraded ccqel code, where we introduced the dependence of the transfer coupling strength on the transferred particle and experimental -value distributions. Because of the availability of distributions measured at the two beam energies of 96 and 103 MeV, two sets of transfer coupling strength were determined for each transfer channel. The study with the upgraded code indicates transfers with negative ground-to-ground states value as the leading cause of the smearing of the barrier distribution. At the beam energy of 103 MeV, the smoothing of is influenced mainly by the one-neutron pickup. On the other hand, the one-neutron pickup and one-proton stripping transfers are dominant for the beam energy of 96 MeV. These results highlight the importance of the transfer coupling dependence on the experimental -value distribution and, consequently, on the dissipation of the projectile kinetic energy.- 4 September 2023
- Published by Elsevier in Journal of Magnetic Resonance
- Vol. 355, 107541
Automated component analysis in DOSY NMR using information criteria
- 4 September 2023
- Published by Elsevier in Journal of Magnetic Resonance
- Vol. 355, 107541
Automated component analysis in DOSY NMR using information criteria
- Open Access
- 21 February 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (3) , 125
Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms
Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.
- 21 February 2023
- Published by MDPI AG in Algorithms
- Vol. 16 (3) , 125
Open AccessAssessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms
Development of energy-efficient and high-performance bioreactors requires progress in methods for assessing the key parameters of the biosynthesis process. With a wide variety of approaches and methods for determining the phase contact area in gas–liquid flows, the question of obtaining its accurate quantitative estimation remains open. Particularly challenging are the issues of getting information about the mass transfer coefficients instantly, as well as the development of predictive capabilities for the implementation of effective flow control in continuous fermentation both on the laboratory and industrial scales. Motivated by the opportunity to explore the possibility of applying classical and non-classical computer vision methods to the results of high-precision video records of bubble flows obtained during the experiment in the bioreactor vessel, we obtained a number of results presented in the paper. Characteristics of the bioreactor’s bubble flow were estimated first by classical computer vision (CCV) methods including an elliptic regression approach for single bubble boundaries selection and clustering, image transformation through a set of filters and developing an algorithm for separation of the overlapping bubbles. The application of the developed method for the entire video filming makes it possible to obtain parameter distributions and set dropout thresholds in order to obtain better estimates due to averaging. The developed CCV methodology was also tested and verified on a collected and labeled manual dataset. An onwards deep neural network (NN) approach was also applied, for instance the segmentation task, and has demonstrated certain advantages in terms of high segmentation resolution, while the classical one tends to be more speedy. Thus, in the current manuscript both advantages and disadvantages of the classical computer vision method (CCV) and neural network approach (NN) are discussed based on evaluation of bubbles’ number and their area defined. An approach to mass transfer coefficient estimation methodology in virtue of obtained results is also represented.