Modeling Prominence Formation in 2.5D
نویسندگان
چکیده
منابع مشابه
Magnetic Reconnection Models of Prominence Formation
To investigate the hypothesis that prominences form by magnetic reconnection between initially distinct flux systems in the solar corona, we simulate coronal magnetic field evolution when two flux systems are driven together by boundary motions. In particular, we focus on configurations similar to those in the quiescent prominence formation model of Martens and Zwaan. We find that: reconnection...
متن کاملThe Dynamic Formation of Prominence Condensations
We present simulations of a model for the formation of a prominence condensation in a coronal loop. The key idea behind the model is that the spatial localization of loop heating near the chromosphere leads to a catastrophic cooling in the corona (Antiochos & Klimchuk 1991). Using a new adaptive grid code, we simulate the complete growth of a condensation, and find that after ∼ 5, 000 s it reac...
متن کاملCompositional Modeling of Wax Formation in Petroleum Mixtures
Heavy organics deposition is a common problem in oil industry, especially in oil production, transportation and processing. Wax or solid paraffin series are examples of heavy organics that deposit. Precipitation and crystallization of wax causes major difficulties in different processes. Based on multi-solid theory, a basic model is modified in this paper for wax precipitation in different oils...
متن کاملProminence Formation Associated with an Emerging Helical Flux Rope
The formation and evolution process and magnetic configuration of solar prominences remain unclear. In order to study the formation process of prominences, we examine continuous observations of a prominence in NOAA AR 10953 with the Solar Optical Telescope on the Hinode satellite. As reported in our previous Letter, we find a signature suggesting that a helical flux rope emerges from below the ...
متن کاملModeling phrasing and prominence using deep recurrent learning
Models for the prediction of prosodic events, such as pitch accents and phrasal boundaries, often rely on machine learning models that combine a set of input features aggregated over a finite, and usually short, number of observations to model context. Dynamic models go a step further by explicitly incorporating a model of state sequence, but even then, many practical implementations are limite...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the International Astronomical Union
سال: 2013
ISSN: 1743-9213,1743-9221
DOI: 10.1017/s1743921313011319