基于机器学习的纳维-斯托克斯方程高阶离散化及其在叶轮机械气动声学中的应用博士研究员
Genuinely high-order discretisation of Navier-Stokes equations using machine learning and its application to turbomachinery aeroacoustics
原帖优先：申请材料、截止时间与资格以原帖和学校官方说明为准。
博士招生与资助公告
- 申请年度
- 2027；原帖说明：How to apply Apply now You need to: • choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences • select Full time or Part time • search for programme PhD Engineering & the Environment (7175) • add name of the supervisor in section 2 of the application Applications should include: • your CV (resumé) • 2 academic references • degree transcripts and certificates to date • English language qualification (if applicable)
- 最近申请截止
- 2027-09-30
- 全部申请截止
- （Europe/London）
- 资助原文
- Fees and funding We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students. Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants. Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants. Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered. For more information, please visit our postgraduate research funding pages.
- 资助条件
- 原帖为竞争性或附条件资助说明，未确认本项目获资助及申请人能获奖；不作为保证全奖。
- 原帖材料说明
- • your CV (resumé)
- • 2 academic references
- • degree transcripts and certificates to date
- • English language qualification (if applicable)
确定性信息来自对应版本的完整原帖。原帖只提供日期，日末和学校当地时区为本站转换假设。筛选使用最后申请截止；资助与录取以学校审核结果为准。
AI 中文速览
- 研究内容
- 本项目旨在利用最新的机器学习技术，开发高度准确且稳定的计算方法来数值求解纳维-斯托克斯方程，构建真正的离散高阶框架，并与劳斯莱斯股份有限公司合作，将其应用于叶轮机械气动声学的挑战性问题中。
- 申请条件
- 申请者需持有英国2:1荣誉学位或同等国际学历，具备扎实的数学基础、全面的空气动力学、湍流和可压缩流知识，以及计算机编程经验。
- 待遇
- 学校为英国本土及国际学生提供一系列资助机会，竞争性学生奖学金通常涵盖英国本土学费以及生活费津贴。
- 申请方式
- 申请人需通过大学系统在线申请，选择研究型项目、2027/28学年、工程与物理科学学院，搜索PhD Engineering & the Environment (7175)并指定导师姓名。
- 材料清单
- 简历 (CV)
- 2份学术推荐信
- 至今的学位成绩单和证书
- 英语语言资格证明（如适用）
由 gemini-2.5-flash-lite 生成，博士岗判定置信度 100%。
岗位信息
- 最终轮次截止
- 2027-09-30
- 学科
- 工程
- 合同类型
- 项目资助
- 本站收录
- 内容更新
- 原帖导师候选（待核验）
- Jae Wook Kim, Long Wu
- 来源
- 南安普顿大学博士研究项目招生 · 最近核对 2026-10-24
- 详情核验
判定依据（原文摘录）
- 确认入学要求为2:1荣誉学位（非仅学士）
A UK 2:1 honours degree or its international equivalent.
原文
View all current projects
Postgraduate research project
Genuinely high-order discretisation of Navier-Stokes equations using machine learning and its application to turbomachinery aeroacoustics
Funding
Competition funded
Competition funded
View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree
2:1 honours degree
View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date
30 Sep 2027
On this page
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
Potential supervisors
Entry requirements
Fees and funding
How to apply
Contact us
About the project
This project aims to develop highly accurate and stable computational methodology to numerically solve Navier-Stokes equations and showcase its efficacy by applying it to a challenging problem of turbomachinery aeroacoustics in partnership with Rolls-Royce plc.
At the forefront of research on aerodynamics and aeroacoustics, we need highly accurate and reliable Computational Fluid Dynamics (CFD) methodology to numerically solve Navier-Stokes equations. Conventional CFD tools are, however, based on low-order discretisation which provides a good level of numerical stability but requires a very large number of grid points to compensate low resolution. As a consequence, higher-order discretisation has become popular in academia since it is substantially more efficient and accurate. The main issue with the high-order approaches so far is that their numerical stability is so poor that they are not widely applicable, particularly when complex geometry is involved. Also, almost all of them accompany cumbersome lower-order boundary treatment which eventually degrades the overall order of accuracy. In order to fix this issue and create a genuinely high-order discretisation framework, the lead supervisor of the project (Professor Kim) has done some foundation work.
In this project, we aim to complete the work using the latest Machine Learning technology to search and achieve the maximum level of stability and genuinely high-order discretisation framework. The new discretisation framework will be incorporated into CANARD (Compressible Aerodynamics & Aeroacoustics Research coDe) developed by Professor Kim at the University of Southampton, and its ultimate performance will be showcased by applying it to a challenging problem of turbomachinery aeroacoustics in partnership with Rolls-Royce plc. The computational simulations will be performed in massively parallel supercomputers (UK national supercomputer CIRRUS at the University of Edinburgh and a local IRIDIS-7 at the University of Southampton).
Training for the main code (CANARD) and massively parallel supercomputing will be provided in the initial stage. Also,You'll have opportunities to attend taught courses and external workshops to develop other essential skills including Machine Learning and Neural Network.
The School of Engineering is committed to promoting equality, diversity inclusivity as demonstrated by our Athena SWAN award. We welcome all applicants regardless of their gender, ethnicity, disability, sexual orientation or age, and will give full consideration to applicants seeking flexible working patterns and those who have taken a career break. The University has a generous maternity policy, onsite childcare facilities, and offers a range of benefits to help ensure employees’ well-being and work-life balance. The University of Southampton is committed to sustainability and has been awarded the Platinum EcoAward.
Potential supervisors
Lead supervisor
Professor Jae Wook Kim
PhD, FRAeS, FHEA, CEng.
Professor of Aerodynamics & Aeroacoustics
Research interests
• Aerodynamics
• Aeroacoustics
• Compressible Fluid Flow
Supervisors
Dr Long Wu
PhD, BEng
Senior Research Fellow
Research interests
• Aeroacoustics
• Aerodynamics
• High-Order Numerical Methods
Entry requirements
A UK 2:1 honours degree or its international equivalent.
Essential skills:
• solid mathematical background
• comprehensive knowledge of aerodynamics, turbulence and compressible flow
• experience in computer programming
Fees and funding
We offer a range of funding opportunities for both UK and international students. Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Competition-based Presidential Bursaries from the University cover the difference between overseas and UK fees for top-ranked applicants.
Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs for top-ranked applicants.
Funding will be awarded on a rolling basis, so apply early for the best opportunity to be considered.
For more information, please visit our postgraduate research funding pages.
How to apply
Apply now
You need to:
• choose programme type (Research), 2027/28, Faculty of Engineering and Physical Sciences
• select Full time or Part time
• search for programme PhD Engineering & the Environment (7175)
• add name of the supervisor in section 2 of the application
Applications should include:
• your CV (resumé)
• 2 academic references
• degree transcripts and certificates to date
• English language qualification (if applicable)
Contact us
Faculty of Engineering and Physical Sciences
For questions about applying, email our Doctoral College (doctoralcollege-admissions@soton.ac.uk).
Project leader
For an initial conversation, email Professor Jae Wook Kim (J.W.Kim@soton.ac.uk).