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Dynamics
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Recent Advances in Dynamic Phenomena—2nd Edition
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Recent Advances in Dynamic Phenomena—2nd Edition

A special issue of Dynamics (ISSN 2673-8716).

Deadline for manuscript submissions: 31 December 2024 | Viewed by 3619

Special Issue Editor

Dr. Christos Volos

E-Mail Website
Guest Editor
Laboratory of Nonlinear Systems, Circuits & Coplexity (LaNSCom), Department of Physics, Aristotle University of Thessaloniki, GR-54124 Thessaloniki, Greece
Interests: electrical and electronics engineering; mathematical modeling; control theory; engineering, applied and computational mathematics; numerical analysis; mathematical analysis; numerical modeling; modeling and simulation; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The wonders of dynamic phenomena encapsulate the intricate motions and effects found in the fabric of nature. A closer examination reveals these dynamic occurrences across a spectrum of disciplines, encompassing physical, chemical, and biological systems. Their manifestation is attributed to a confluence of inertial forces and the distinctive characteristics inherent in various systems. Furthermore, the allure of intriguing dynamics extends to mechanical and electronic systems, integral components in applications such as robotics, aircraft, and vehicles.

This call invites contributions to a Special Issue dedicated to showcasing recent advances in understanding dynamic phenomena spanning from the minutest scales to the grandest. This includes the exploration of mechanism dynamics at the cellular level within biological systems, phenomena within the Earth's water or atmosphere, and those exhibited in mechanical and electronic systems. Researchers are encouraged to submit their work encompassing both theoretical and experimental findings.

Submissions are encouraged from diverse fields, including but not limited to:

  • Aerodynamics;
  • Biological systems and networks;
  • Cell dynamics;
  • Climate dynamics;
  • Dynamic cycles of birds and animals;
  • Dynamics in mechanics;
  • Fluid dynamics;
  • Gas dynamics;
  • Nonlinear dynamics and chaos;
  • Nuclear dynamics;
  • Quantum mechanics and electrodynamics;
  • Terrestrial dynamics.

Dr. Christos Volos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Dynamics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • chaos
  • dynamic phenomena
  • fluids
  • gas dynamics
  • mechanics
  • nonlinear systems
  • nuclear dynamics
  • quantum mechanics
  • electrodynamics
  • terrestrial dynamics

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Published Papers (3 papers)

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Research

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16 pages, 8922 KiB  
Article
SPH Simulation of Molten Metal Flow Modeling Lava Flow Phenomena with Solidification
by Shingo Tomita, Joe Yoshikawa, Makoto Sugimoto, Hisaya Komen and Masaya Shigeta
Dynamics 2024, 4(2), 287-302; https://doi.org/10.3390/dynamics4020017 - 19 Apr 2024
Viewed by 1152
Abstract
Characteristic dynamics in lava flows, such as the formation processes of lava levees, toe-like tips, and overlapped structures, were reproduced successfully through numerical simulation using the smoothed particle hydrodynamics (SPH) method. Since these specific phenomena have a great influence on the flow direction [...] Read more.
Characteristic dynamics in lava flows, such as the formation processes of lava levees, toe-like tips, and overlapped structures, were reproduced successfully through numerical simulation using the smoothed particle hydrodynamics (SPH) method. Since these specific phenomena have a great influence on the flow direction of lava flows, it is indispensable to elucidate them for accurate predictions of areas where lava strikes. At the first step of this study, lava was expressed using a molten metal with known physical properties. The computational results showed that levees and toe-like tips formed at the fringe of the molten metal flowing down on a slope, which appeared for actual lava flows as well. The dynamics of an overlapped structure formation were also simulated successfully; therein, molten metal flowed down, solidified, and changed the surface shape of the slope, and the second molten metal flowed over the changed surface shape. It was concluded that the computational model developed in this study using the SPH method is applicable for simulating and clarifying lava flow phenomena. Full article
(This article belongs to the Special Issue Recent Advances in Dynamic Phenomena—2nd Edition)
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18 pages, 5288 KiB  
Article
System Identification Using Self-Adaptive Filtering Applied to Second-Order Gradient Materials
by Thomas Kletschkowski
Dynamics 2024, 4(2), 254-271; https://doi.org/10.3390/dynamics4020015 - 7 Apr 2024
Viewed by 768
Abstract
For many engineering applications, it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in [...] Read more.
For many engineering applications, it is sufficient to use the concept of simple materials. However, higher gradients of the kinematic variables are taken into account to model materials with internal length scales as well as to describe localization effects using gradient theories in finite plasticity or fluid mechanics. In many approaches, length scale parameters have been introduced that are related to a specific micro structure. An alternative approach is possible, if a thermodynamically consistent framework is used for material modeling, as shown in the present contribution. However, even if sophisticated and thermodynamically consistent material models can be established, there are still not yet standard experiments to determine higher order material constants. In order to contribute to this ongoing discussion, system identification based on the method of self-adaptive filtering is proposed in this paper. To evaluate the effectiveness of this approach, it has been applied to second-order gradient materials considering longitudinal vibrations. Based on thermodynamically consistent models that have been solved numerically, it has been possible to prove that system identification based on self-adaptive filtering can be used effectively for both narrow-band and broadband signals in the field of second-order gradient materials. It has also been found that the differences identified for simple materials and gradient materials allow for condition monitoring and detection of gradient effects in the material behavior. Full article
(This article belongs to the Special Issue Recent Advances in Dynamic Phenomena—2nd Edition)
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Review

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31 pages, 5192 KiB  
Review
Cupolets: History, Theory, and Applications
by Matthew A. Morena and Kevin M. Short
Dynamics 2024, 4(2), 394-424; https://doi.org/10.3390/dynamics4020022 - 13 May 2024
Viewed by 945
Abstract
In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to [...] Read more.
In chaos control, one usually seeks to stabilize the unstable periodic orbits (UPOs) that densely inhabit the attractors of many chaotic dynamical systems. These orbits collectively play a significant role in determining the dynamics and properties of chaotic systems and are said to form the skeleton of the associated attractors. While UPOs are insightful tools for analysis, they are naturally unstable and, as such, are difficult to find and computationally expensive to stabilize. An alternative to using UPOs is to approximate them using cupolets. Cupolets, a name derived from chaotic, unstable, periodic, orbit-lets, are a relatively new class of waveforms that represent highly accurate approximations to the UPOs of chaotic systems, but which are generated via a particular control scheme that applies tiny perturbations along Poincaré sections. Originally discovered in an application of secure chaotic communications, cupolets have since gone on to play pivotal roles in a number of theoretical and practical applications. These developments include using cupolets as wavelets for image compression, targeting in dynamical systems, a chaotic analog to quantum entanglement, an abstract reducibility classification, a basis for audio and video compression, and, most recently, their detection in a chaotic neuron model. This review will detail the historical development of cupolets, how they are generated, and their successful integration into theoretical and computational science and will also identify some unanswered questions and future directions for this work. Full article
(This article belongs to the Special Issue Recent Advances in Dynamic Phenomena—2nd Edition)
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