Chrome Extension
WeChat Mini Program
Use on ChatGLM

Strong Bow Shocks: Turbulence and an Exact Self-Similar Asymptotic

The Astrophysical Journal(2024)

NYU

Cited 0|Views6
Abstract
We show that strong bow shocks are turbulent and nonuniversal near the head but asymptote to a universal, steady, self-similar, and analytically solvable flow in the downstream. The turbulence is essentially 3D and has been confirmed by a 3D simulation. The asymptotic behavior is confirmed with high-resolution 2D and 3D simulations of a cold uniform wind encountering both a solid spherical obstacle and stellar wind. This solution is relevant in the context of (i) probing the kinematic properties of observed high-velocity compact bodies—e.g., runaway stars and/or supernova ejecta blobs—flying through the interstellar medium; and (ii) constraining stellar bow shock luminosities invoked by some quasiperiodic eruption models.
More
Translated text
Key words
Perturbation methods,Astrophysical fluid dynamics,Interstellar medium,Stellar bow shocks
PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Jorge A. Tarango-Yong,William J. Henney
2018

被引用9 | 浏览

B. T. Tsurutani, R. G. Stone
1985

被引用5 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest