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2. SUBJECT FIELDS FOR JOINT RESEARCH

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  • Announcement for Joint Research Proposals, 2024
  • 2. Subject Fields for Joint Research

2-1. Designated Joint Research

( 1 ) Designated Joint Research 1 ( International )

Subject-1 : Multidisciplinary study of wave dynamics in rotating stratified fluids
Integrator : Yohei Onuki (Earth Environment Dynamics)
Outlines : In the Earth’s oceans and planetary atmospheres, various waves such as gravity and Rossby waves exist due to buoyancy forces inherent in stably stratified fluids or Coriolis forces originating from a rotation of the system. They carry energy and momentum while interacting with an ambient flow field and reflecting and scattering at the boundaries, thus playing essential roles in the environmental system at a range of scales. This joint research explores fundamental wave dynamics in the ocean and atmosphere, aiming to improve the parameterizations in global circulation models and develop data analysis techniques useful for next-generation satellite observations. Multidisciplinary approaches centered on experiments, theory, and numerical simulations are welcomed.

( 2 ) Designated Joint Research 2 ( Cross-Disciplinary )

Subject-2 : Synthetic diagnostics combining measurement, simulation and modeling
Integrator : Naohiro Kasuya (Nuclear Fusion Dynamics)
Outlines : Interpretation of measured signals in experiments usually includes complicated processes with some assumptions, regardless of the research fields. Synthetic diagnostics utilize simulation data to compensate for insufficient information from experiments, and can give quantitative comparison between experiments and simulations. One example is computational imaging using global simulation data of plasma turbulence. Theoretical analysis gives a guide for the interpretation. Recent development in data-driven science is favorable to handle a large amount of data both in experiments and simulations. Therefore, collaboration of experimental measurement, complex simulation and data-driven modeling should be strongly encouraged. This designated joint research aims at developing sophisticated methods to capture micro and macroscopic features in liquid, gas and plasma by giving comprehensive understanding of the synthetic diagnostics carried out in the individual fields.

( 3 ) Designated Joint Research 3 ( Cross-Disciplinary )

Subject-3 : Developing new research fields by integrating experimental/measurement science and computational science
Integrator : Keiya Yumimoto (Earth Environment Dynamics)
Yusuke Kosuga (Nuclear Fusion Dynamics)
Yoshihiro Kangawa (Renewable Energy Dynamics)
Outlines : In scientific research to date, there have been two types of science: experimental and measurement science, which involves observing, measuring, and visualizing research objects, and computational science, which involves reproducing and predicting phenomena using numerical models built on physical and scientific theory. In recent years, the integration of the two sciences by the use of machine learning, mathematical statistics, and data assimilation techniques has led to the acquisition of knowledge that cannot be obtained independently, and the optimization of experimental settings and model parameters. The RIAM has a variety of experimental and research facilities as well as large-scale computing equipment for collaborative research. In this specific research project, we will use these facilities to develop a new research field by integrating experimental, measurement, and computational science-based researchers while communicating with each other.

( 4 ) Designated Joint Research 4 ( Cross-Disciplinary )

Subject-4 : Data Processing Automation and Optimization using IoT and Big-Data Technologies
Integrator : Makoto Hasegawa (Nuclear Fusion Dynamics)
Zhu Hongzhong (Renewable Energy Dynamics)
Outlines : With the advance of Internet-of-Things (IoT) technology, massive amounts of unstructured data are being generated by devices and sensors in our surroundings. Simultaneously, effective methods for data collection, storage, and analysis are rapidly evolving along with the progression of big-data technologies. Data automation and optimization in data processing have become pivotal challenges across various fields.

This specific research, drawing from automatic control technologies developed through experimental studies, plays a significant role in enhancing the development, accumulation, and dissemination of new technologies such as real-time data processing. The research finds applications in a wide range of fields, including data-driven control and renewable energy development. It aims to enhance problem-solving capabilities and competitiveness by fostering innovation in multi-disciplinary studies.

 

 

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