Updated on 2026/05/30

写真a

 
FUJIO, Chihiro
 
Affiliation
Faculty of Science and Engineering, School of Fundamental Science and Engineering
Job title
Assistant Professor(non-tenure-track)
Degree
Doctor of Philosophy in Engineering ( 2024.09 Kyushu University )

Education Background

  • 2021.10
    -
    2024.09

    Kyushu University   Graduate School of Engineering   Department of Aeronautics and Astronautics  

  • 2020.04
    -
    2021.09

    Kyushu University   Graduate School of Engineering   Department of Aeronautics and Astronautics  

  • 2016.04
    -
    2020.03

    Kyushu University   School of Engineering   Department of Aeronautics and Astronautics  

Professional Memberships

  • 2026.04
    -
    Now

    日本機械学会

  • 2025.10
    -
    Now

    American Institute of Aeronautics and Astronautics

  • 　
    -
    Now

    日本航空宇宙学会

Research Areas

  • Aerospace engineering   宇宙システム工学 / Intelligent informatics   機械学習

Research Interests

  • Space trainsportation

  • Air-breathing engine

  • Machine learning

  • Multi-Objective Optimisation

  • Data mining

Awards

  • Young Researcher Award

    2026.04   The Japan Society for Aeronautical and Space Sciences   Analytical Characterization and Multi-Objective Optimization of Scramjet Intake Performance

    Winner： Chihiro Fujio

  • JSPS Ikushi Prize

    2025.03   Japan Society for the Promotion of Science  

    Winner： Chihiro Fujio

 

Papers

  • Design exploration of hypersonic air-breathing vehicle including airframe and air inlet using deep-learning flowfield prediction

    Chihiro Fujio, Hideyuki Taguchi

    CEAS Space Journal    2026.05  [Refereed]

    Authorship：Lead author, Corresponding author

    　View Summary

    Abstract

    Hypersonic airbreathing vehicles are promising candidates for high-speed point-to-point transportation and first stage of space transportation system. Airbreathing engines are often mounted under the vehicle to utilize the compression of shock waves formed by the vehicle shape. However, the design of airbreathing engines and vehicles’ airframe are often conducted in a separate manner. This does not always lead to a global optimization of hypersonic vehicle design considering aero-propulsive balance and stability. The present study develops a fast approach for full-vehicle aero-propulsive performance evaluation to realize multi-objective optimization of hypersonic vehicle considering aero-propulsive interaction. To provide a global reconstruction of the coupled flowfield behavior across the design space, a deep-learning model is employed to predict scramjet flowfields due to its capability to capture strongly nonlinear features with high computational efficiency for high-dimensional data such as flowfields, while a local surface inclination method is used to evaluate the aerodynamic performance of the airframe. The proposed framework is applied to a representative multi-objective optimization problem, demonstrating its capability for simultaneous airframe–propulsion design exploration.

    DOI

  • Numerical Investigation of a Supersonic Wind Tunnel Diffuser Optimization

    Riccardo Nicoletti, Francesco Margani, Luca Armani, Antonella Ingenito, Chihiro Fujio, Hideaki Ogawa, Seoeum Han, Bok Jik Lee

    Aerospace   12 ( 5 ) 366 - 366  2025.04  [Refereed]

    　View Summary

    The objective of this study is to enhance the methodology for the design of a supersonic wind tunnel, improving the process with advanced computational techniques. The supersonic wind tunnel is intended to operate within a flight envelope of Mach 2.5 to 4 and altitudes between 18 and 20 km; this study focuses on the operative condition of Mach 3.5. The research is based on computational fluid dynamics, enabling a deeper understanding of fluid flow phenomena that can deteriorate the operability of the wind tunnel. Additionally, a detailed mesh independence study has been conducted to ensure the reliability and robustness of the computational results. These new analyses allowed for a more comprehensive optimization in the state of the art of tunnel geometry and operational conditions, further enhancing the ability to sustain supersonic flow for extended durations. Particular attention was given to the second throat, which plays a crucial role in the overall performance of the facility, especially during the start-up process. Its design has been refined to improve efficiency by reducing the minimum starting pressure.

    DOI

    Scopus

    4
    Citation
    (Scopus)

  • Analytical Characterization and Multi-Objective Optimization of Scramjet Intake Performance

    Chihiro FUJIO, Hideaki OGAWA

    TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES   68 ( 1 ) 57 - 67  2025.01  [Refereed]

    Authorship：Lead author, Corresponding author

    DOI

    Scopus

  • Optimization and data mining for shock-induced mixing enhancement inside scramjet using stochastic deep-learning flowfield prediction

    Chihiro Fujio, Hideaki Ogawa

    Aerospace Science and Technology   154   109513 - 109513  2024.11  [Refereed]

    Authorship：Lead author, Corresponding author

    DOI

    Scopus

    14
    Citation
    (Scopus)

  • Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction

    Chihiro Fujio, Hideaki Ogawa

    Aerospace Science and Technology   150   109183 - 109183  2024.07  [Refereed]

    Authorship：Lead author, Corresponding author

    DOI

    Scopus

    12
    Citation
    (Scopus)

  • Fast and reliable prediction of scramjet flowfields via Gaussian process latent variable model and deep learning

    Chihiro Fujio, Kento Akiyama, Hideaki Ogawa

    Physics of Fluids   35 ( 4 )  2023.04  [Refereed]

    Authorship：Lead author

    　View Summary

    Fast and accurate prediction of high-speed flowfields is of particular interest to researchers in fluid science and engineering to enable efficient design exploration and knowledge discovery. The reliability of prediction is another important metric for the performance of prediction models. While predictive modeling approaches with and without reduced-order modeling (ROM) via machine learning techniques have been proposed, they are inherently subject to loss of information for ROM-based approaches and substantial computational costs in modeling for non-ROM-based approaches. This paper proposes an accurate ROM-based predictive framework with minimum information loss enabled by incorporating Gaussian process latent variable modeling (GPLVM) and deep learning. The stochastic nature of GPLVM allows for uncertainty quantification that indicates the degree of prediction error or reliability of prediction without requiring validation data. The applicability for supersonic/hypersonic viscous flowfields has been examined for two cases including axisymmetric intakes and two-dimensional fuel injection in scramjet engines by comparison with other predictive models. Comparable or superior prediction accuracy over the other models has been achieved by the proposed approaches, demonstrating its high potential to serve as a new competent, data-driven technique for fast, accurate, and reliable prediction of scramjet flowfields.

    DOI

    Scopus

    26
    Citation
    (Scopus)

  • Deep-learning prediction and uncertainty quantification for scramjet intake flowfields

    Chihiro Fujio, Hideaki Ogawa

    AEROSPACE SCIENCE AND TECHNOLOGY   130  2022.11  [Refereed]

    Authorship：Lead author

    DOI

    Scopus

    38
    Citation
    (Scopus)

  • Nozzle design optimization for supersonic wind tunnel by using surrogate-assisted evolutionary algorithms

    Masanobu Matsunaga, Chihiro Fujio, Hideaki Ogawa, Yoshitaka Higa, Taro Handa

    AEROSPACE SCIENCE AND TECHNOLOGY   130  2022.11  [Refereed]

    DOI

    Scopus

    21
    Citation
    (Scopus)

  • Physical insights into multi-point global optimum design of scramjet intakes for ascent flight

    Chihiro Fujio, Hideaki Ogawa

    ACTA ASTRONAUTICA   194   59 - 75  2022.05  [Refereed]

    Authorship：Lead author

    DOI

    Scopus

    22
    Citation
    (Scopus)

  • Design optimization and off-design performance analysis of axisymmetric scramjet intakes for ascent flight

    Shuvayan Brahmachary, Chihiro Fujio, Mehmet Aksay, Hideaki Ogawa

    PHYSICS OF FLUIDS   34 ( 3 )  2022.03  [Refereed]

    DOI

    Scopus

    26
    Citation
    (Scopus)

  • Physical insight into axisymmetric scramjet intake design via multi-objective design optimization using surrogate-assisted evolutionary algorithms

    Chihiro Fujio, Hideaki Ogawa

    AEROSPACE SCIENCE AND TECHNOLOGY   113  2021.06  [Refereed]

    Authorship：Lead author

    DOI

    Scopus

    33
    Citation
    (Scopus)

  • Numerical investigation of axisymmetric intake flowfield and performance for scramjet-powered ascent flight

    Chihiro Fujio, Shuvayan Brahmachary, Hideaki Ogawa

    AEROSPACE SCIENCE AND TECHNOLOGY   111  2021.04  [Refereed]

    Authorship：Lead author

    DOI

    Scopus

    22
    Citation
    (Scopus)

  • Multi-objective design optimization and analysis of streamline-traced intakes for scramjet-powered ascent flight

    Chihiro Fujio, Hideaki Ogawa

    AIAA Scitech 2021 Forum     1 - 24  2021

  • Multi-point design optimization of a high-performance intake for scramjet-powered ascent flight

    Shuvayan Brahmachary, Chihiro Fujio, Hideaki Ogawa

    AEROSPACE SCIENCE AND TECHNOLOGY   107  2020.12  [Refereed]

    DOI

    Scopus

    37
    Citation
    (Scopus)

▼display all

Presentations

  • Development of a Performance Analysis Tool for Turbo-Ramjet Engines

    Chihiro Fujio, Hideyuki Taguchi

    Presentation date： 2026.03

    Event date：
    2026.03
    　
    　

  • Trajectory Optimization and Design Requirement Study of a Space Transportation System Using a Scramjet Engine

    Chihiro Fujio, Masaru Koga, Shun Takahashi, Tatsushi Isono, Masahiro Takahashi, Hideyuki Taguchi, Masao Takegoshi, Sadatake Tomioka

    Presentation date： 2026.03

    Event date：
    2026.03
    　
    　

  • 極超音速複合サイクルエンジンの概念設計

    藤尾秩寛, 田口秀之, 小林亮太, 佐藤哲也

    第６９回宇宙科学技術連合講演会 

    Presentation date： 2025.11

    Event date：
    2025.11
    　
    　

  • Design exploration of hypersonic vehicle airframe and air-breathing engine using deep-learning flowfield prediction

    Chihiro Fujio, Hideyuki Taguchi

    4th International Conference on High-Speed Vehicle Science & Technology 

    Presentation date： 2025.09

    Event date：
    2025.09
    　
    　

  • Asteroid multi-flyby exploration concept using micro solar sails and its trajectory design

    Chihiro Fujio, Yuki Takao, Tsubasa Ozawa, Toshihiro Chujo, Naoya Ozaki, Ryuki Hyodo

    The 68th Space Sciences and Technology Conference 

    Presentation date： 2024.11

    Event date：
    2024.11
    　
    　

  • Numerical Investigation of Shock Induced Mixing Enhancement in Cavity-Based Scramjet Combustor

    Tomoaki Nara, Chihiro Fujio, Hideaki Ogawa

    The 3rd International Conference on High-Speed Vehicle Science and Technology 

    Presentation date： 2024.04

    Event date：
    2024.04
    　
    　

  • Knowledge Discovery on Cavity-Based Scramjet Combustor Design via Stochastic-Surrogate-Assisted Multi-Objective Optimization

    Chihiro Fujio, Sasi Kiran Palateerdham, Lakshmi Narayana, Phaneendra Peri, Hideaki Ogawa, Antonella Ingenito

    The 3rd International Conference on High-Speed Vehicle Science and Technology 

    Presentation date： 2024.04

    Event date：
    2024.04
    　
    　

  • 宇宙輸送用スクラムジェットインテークの多迎角同時モデルベース設計最適化

    藤尾秩寛, 大宮康平, 奈良知璃, 柴北碧, 小川秀朗, 内山絵里香

    第63回航空原動機・宇宙推進講演会/北部支部2024年講演会 ならびに第5回再使用型宇宙輸送系シンポジウム 

    Presentation date： 2024.03

    Event date：
    2024.03
    　
    　

  • Multi-Objective Design Optimization of Shock-Induced Mixing Enhancement via Evolutionary Algorithms Assisted by Data-Driven Approaches

    Chihiro Fujio, Hideaki Ogawa

    74th International Astronautical Congress (IAC) 

    Presentation date： 2023.10

    Event date：
    2023.10
    　
    　

  • Characterization of Shock-Induced Mixing Enhancement for Transverse Injection in Scramjet Engines

    Chihiro Fujio, Hideaki Ogawa

    25th AIAA International Space Planes and Hypersonic Systems and Technologies Conference (AIAA Hypersonics 2023) 

    Presentation date： 2023.05

    Event date：
    2023.05
    -
    2023.06

  • 深層学習による流動予測を用いたスクラムジェットインテークの多目的最適化

    藤尾秩寛, 小川秀朗

    第54期年会講演会 

    Presentation date： 2023.04

    Event date：
    2023.04
    　
    　

  • Inverse Design and Sensitivity Analysis of Scramjet Intake Using Deep Learning

    Chihiro Fujio, Hideaki Ogawa

    11th Asian Joint Conference on Propulsion and Power (AJCPP 2023) 

    Presentation date： 2023.03

    Event date：
    2023.03
    　
    　

  • Multi-Objective Design Optimization of Scramjet Intakes via Evolutionary Algorithms Assisted by Multi-Dimensional Predictive Modeling Based on Deep Learning

    Chihiro Fujio, Hideaki Ogawa

    2nd International Conference on High-Speed Vehicle Science & Technology 

    Event date：
    2022.09
    　
    　

  • Characterization of Scramjet Intake Flowfields and Performance During Ascent Flight via High-Resolution Numerical Simulation

    Chihiro Fujio, Hideaki Ogawa

    2nd International Conference on High-Speed Vehicle Science & Technology 

    Event date：
    2022.09
    　
    　

  • 深層学習予測に基づくスクラムジェットインテーク 流体挙動の感度解析

    藤尾秩寛, 秋山健人, 小川秀朗

    第 61 回航空原動機・宇宙推進講演会 

    Event date：
    2022.03
    　
    　

  • Scramjet Intake Design Based on Exit Flow Profile via Global Optimization and Deep Learning toward Inverse Design

    Chihiro Fujio, Hideaki Ogawa

    SciTech 2022 - AIAA Science and Technology Forum and Exposition 

    Event date：
    2022.01
    　
    　

  • 深層学習を用いたスクラムジェットインテーク 内部流れ場の予測に関する研究

    藤尾秩寛, 小川秀朗

    第53回流体力学講演会/第39回航空宇宙数値シミュレーション技術シンポジウム 

    Event date：
    2021.06
    -
    2021.07

  • Multi-Objective Design Optimization and Analysis of Streamline-Traced Intakes for Scramjet-Powered Ascent Flight

    SciTech 2021 - AIAA Science and Technology Forum and Exposition 

    Event date：
    2021.01
    　
    　

  • Parametric Characterization of Streamline Traced Intake Design for Scramjet-Powered Ascent Flight

    Event date：
    2020.09
    　
    　

▼display all

Research Projects

  • 深層学習流動予測を用いた極超音速複合サイクルエンジンの制御則構築・システム解析

    日本学術振興会  科学研究費助成事業

    Project Year :

    2026.04
    -
    2029.03
     

    藤尾 秩寛

  • Advanced Flow Prediction of Scramjet Intakes and Data-Driven Inverse Design

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research

    Project Year :

    2023.03
    -
    2025.03
     

 

Syllabus