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Petascale Adaptive Mesh Simulations of Milky Way-type Galaxies and Their Environments

Petascale Adaptive Mesh Simulations of Milky Way-type Galaxies and Their Environments
银河系及其环境的千万亿次自适应网格模拟
批准号:
1514580
负责人:
Brian O'Shea
金额:
$3.15万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-08-01 至 2018-07-31

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中文摘要
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英文摘要
This project seeks answers to several pressing questions about the formation and evolution of galaxies. It does so by using the Blue Waters supercomputer to perform a suite of sophisticated supercomputer simulations. The investigators will address such questions as: (i) How did the earliest progenitors of the Milky Way galaxy form, and where can we find their stellar remnants today? (ii) How does the ionizing radiation produced by massive stars escape from galaxies, and how does it affect the properties of neighboring galaxies? (iii) How does the gas that is critical for star formation get from the cosmic web into the central regions of galaxies, and how is gas returned to the intergalactic medium? (iv) How are magnetic fields seeded and amplified in galaxies, and how are they ejected into (or amplified in) the intergalactic medium?The team includes experts in astrophysics as well as in high performance computing, and is united in the use of a sophisticated numerical tool (the Enzo AMR code) that has already demonstrated its performance on Blue Waters. The team will work with observational astronomer collaborators to apply these simulations to the interpretation of measurements of both local and distant galaxies from current astronomical surveys, and to motivate future observations by the Large Synoptic Survey Telescope and the James Webb Space Telescope.The proposed work promises to have significant impact on scientists in training, who will learn to use cutting-edge numerical tools at the largest possible scale. The project will involve undergraduate students at Michigan State University (through MSU?s REU program, which targets women and under-represented minorities) and postdoctoral researchers in the research efforts. Scientific results from this program will be visualized by staff at the National Center for Supercomputing Applications, and will be disseminated to the public via pre-existing collaborations with planetaria and museums, and via the Internet. In addition, these visualizations will be used as part of outreach talks given by members of this project. Finally, the simulation data produced as a result of this project will be used in computational science courses at Michigan State University, where it will be used to train students in scientific visualization and data analysis techniques. The resulting curricular materials will be made available to the public via the World Wide Web.The specific research methods used in this project include the creation of an extensive library of simulated Milky Way-like galaxies and their environments that can be used to explore a wide range of observable astrophysical phenomena. This will be the first study to perform cosmological simulations of galaxy formation and evolution that include self-consistent treatments of radiation transport and/or magnetohydrodynamics for a statistically significant number of galaxies, and to apply these calculations to the interpretation of recent observations relating to the intergalactic and circumgalactic medium, galactic and extragalactic magnetic fields, and high redshift galaxy formation. Furthermore, the simulation data produced during the course of this project, as well as a wide range of data products, will be made publicly available via the nascent National Data Service. This data will be usable by the astrophysical research community, and will enable researchers to address a much broader range of questions regarding galaxy formation and evolution than can be done as a part of this project alone, thus leveraging the computational resources available on Blue Waters.
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CC* Compute: The MSU Data Machine - a high-memory, GPU-enabled compute cluster for data-intensive and AI applications
  • 批准号:
    2200792
  • 项目类别:
    Standard Grant
  • 资助金额:
    $39.99万
  • 财政年份:
    2022
  • 负责人:
    Brian O'Shea
  • 依托单位:
CC* Networking Infrastructure: A Science DMZ For Quantitative Biology and Precision Agriculture
  • 批准号:
    2018432
  • 项目类别:
    Standard Grant
  • 资助金额:
    $48.31万
  • 财政年份:
    2020
  • 负责人:
    Brian O'Shea
  • 依托单位:
REU Site: iCER ACRES: iCER Advanced Computational Research Experience for Students
  • 批准号:
    1949912
  • 项目类别:
    Standard Grant
  • 资助金额:
    $40.12万
  • 财政年份:
    2020
  • 负责人:
    Brian O'Shea
  • 依托单位:
Travel grant for LRAC proposal AST20004: The role of low collisionality in compressible, magnetized turbulence
  • 批准号:
    2031219
  • 项目类别:
    Standard Grant
  • 资助金额:
    $0.95万
  • 财政年份:
    2020
  • 负责人:
    Brian O'Shea
  • 依托单位:
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