中性流体およびプラズマにおける亜臨界不安定性について

International audience亜臨界不安定性は，非線形不安定性の一種である．亜臨界不安定な系は，線形安定であっても非線形的に不安定となる．特徴として，不安定性が生じるための初期摂動の大きさに閾値が存在し，閾値以下の摂動は減衰し安定化する．亜臨界不安定性は，流体やプラズマにおいて広くみられる現象である．亜臨界不安定性は，乱流や構造形成，異常抵抗性や乱流輸送に本質的なインパクトを与えるため重要な問題である．この解説では，亜臨界不安定性の概念について解説し，様々な物理的局面における研究について紹介する

Oceanographic Data of the 40th Japanese Antarctic Research Expedition from November 1998 to March 1999

The results of oceanographic observations on board the icebreaker "Shirase" and tidal observations at Syowa Station, Antarctica are presented in this report. The oceanographic observations were carried out by the summer party of the 40th Japanese Antarctic Research Expedition (JARE-40) during the austral summer of 1998/1999. The tidal observations were carried out by the winter party of JARE-39 from February 1998 to January 1999

Discrete plane segmentation and estimation from a point cloud using local geometric patterns

International audienceThis paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete geometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. Contrarily to the continuous case, there exist a finite number of local geometric patterns (LGPs) appearing on discrete planes. Moreover, such a LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs can have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate parameters of a discrete plane by minimizing its thickness

Subcritical Instabilities in Neutral Fluids and Plasmas

International audienceIn neutral fluids and plasmas, the analysis of perturbations often starts with an inventory of linearly unstable modes. Then, the nonlinear steady-state is analyzed or predicted based on these linear modes. A crude analogy would be to base the study of a chair on how it responds to infinitesimaly small perturbations. One would conclude that the chair is stable at all frequencies, and cannot fall down. Of course, a chair falls down if subjected to finite-amplitude perturbations. Similarly, waves and wave-like structures in neutral fluids and plasmas can be triggered even though they are linearly stable. These subcritical instabilities are dormant until an interaction, a drive, a forcing, or random noise pushes their amplitude above some threshold. Investigating their onset conditions requires nonlinear calculations. Subcritical instabilities are ubiquitous in neutral fluids and plasmas. In plasmas, subcritical instabilities have been investigated based on analytical models and numerical simulations since the 1960s. More recently, they have been measured in laboratory and space plasmas, albeit not always directly. The topic could benefit from the much longer and richer history of subcritical instability and transition to subcritical turbulence in neutral fluids. In this tutorial introduction, we describe the fundamental aspects of subcritical instabilities in plasmas, based on systems of increasing complexity, from simple examples of a point-mass in a potential well or a box on a table, to turbulence and instabilities in neutral fluids, and finally, to modern applications in magnetized toroidal fusion plasmas

Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates

Deep-learning inverse techniques have attracted significant attention in recent years. Among them, the neural adjoint (NA) method, which employs a neural network surrogate simulator, has demonstrated impressive performance in the design tasks of artificial electromagnetic materials (AEM). However, the impact of the surrogate simulators' accuracy on the solutions in the NA method remains uncertain. Furthermore, achieving sufficient optimization becomes challenging in this method when the surrogate simulator is large, and computational resources are limited. Additionally, the behavior under constraints has not been studied, despite its importance from the engineering perspective. In this study, we investigated the impact of surrogate simulators' accuracy on the solutions and discovered that the more accurate the surrogate simulator is, the better the solutions become. We then developed an extension of the NA method, named Neural Lagrangian (NeuLag) method, capable of efficiently optimizing a sufficient number of solution candidates. We then demonstrated that the NeuLag method can find optimal solutions even when handling sufficient candidates is difficult due to the use of a large and accurate surrogate simulator. The resimulation errors of the NeuLag method were approximately 1/50 compared to previous methods for three AEM tasks. Finally, we performed optimization under constraint using NA and NeuLag, and confirmed their potential in optimization with soft or hard constraints. We believe our method holds potential in areas that require large and accurate surrogate simulators.Comment: 20 pages, 8 figure