利用多物理场优化技术进行增材制造驱动的电机设计

本研究项目探讨利用增材制造技术开发高性能电机，从而提升功率密度、效率及热性能。该项目涉及建立一种设计方法学，综合考虑电磁、热及机械约束条件，并结合多目标优化与实验验证，以提高这些电机的可制造性。

Introduction: Through SRAP, Coventry-based PhD researchers undertake one to two years of research training in Singapore under the joint supervision of researchers from both Coventry and ARTC. The programme offers PhD researchers an exceptional opportunity to gain international research experience while contributing to ambitious, multidisciplinary research addressing global technological and sustainability challenges. This collaborative PhD programme enables researchers to work at the interface of academic research, advanced engineering and industrial innovation. Researchers will engage with expertise and facilities across both institutions, build international research networks, and develop experience in translating advanced research into practical solutions for future mobility and remanufacturing systems. The programme is particularly suited to ambitious researchers seeking to shape next-generation electric mobility systems and circular manufacturing technologies, while gaining valuable research experience across the UK and Singapore. Project details: This PhD project will investigate the use of additive manufacturing (AM) to develop high-performance electrical machines with enhanced power density, efficiency and thermal performance. The research will establish a design methodology that exploits the geometric and material flexibility offered by AM while addressing the electromagnetic, thermal, mechanical and manufacturing constraints associated with practical machine development. Candidate machine architectures, including radial- and axial-flux configurations, will first be assessed to establish suitable baseline designs. These designs will then be investigated using coupled electromagnetic, thermal and mechanical modelling and subsequently refined through multi-objective optimisation. Attention will be given to the interaction between machine geometry, AM material properties, cooling requirements, mechanical integrity and manufacturing constraints. Experimentally measured properties of AM materials will be incorporated into the design process to improve the accuracy and practical relevance of the models. Promising designs will be selected for manufacture and evaluated through component-level and complete-machine testing. The experimental results will be used to assess the benefits and limitations of AM for electrical machine applications and to validate the proposed design methodology. The project will ultimately provide guidance for designing manufacturable, high-performance electrical machines that make effective use of the capabilities of additive manufacturing. Research may include • Machine architecture: investigation of radial- and axial-flux topologies, rotor configurations, slot-pole combinations and winding arrangements. • AM materials: characterisation of electromagnetic, thermal and mechanical properties of additively manufactured materials and assessment of their suitability for electrical machine components. • Multi-physics modelling: development of coupled electromagnetic, thermal and structural models to understand interactions between machine performance, temperature and mechanical loading. • Design optimisation: application of topology and multi-objective optimisation to balance power density, efficiency, losses, thermal performance and material utilisation. • Thermal management: investigation of cooling approaches and machine geometries enabled by AM to improve heat transfer and continuous power capability. • Prototype development, experimental validation and performance assessment You’re invited to become part of the Coventry Centre for Doctoral Training in AI (C-CDT AI ): a dynamic, collaborative hub uniting AI staff and PGR researchers from across the University. As a C-CDT member you’ll benefit from structured AI training (from core research methods to ethics and innovation), interdisciplinary peer networks, specialist supervision, shared GPU/cloud resources, workshops, seminars and industry-focused challenge projects. This additional training and community, complements your home research centre programme, accelerates your technical and professional development, and connects you with a broad AI research ecosystem tackling impactful problems across sectors.

Entry requirements: A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average. PLUS The potential to engage in innovative research and to complete the PhD within 4 years. A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component). Additional Requirements: Candidates should normally hold, or expect to obtain, a strong first degree and/or master's qualification in a relevant STEM discipline. Suitable backgrounds may include: • Electrical and Electronic Engineering • Power Engineering • Mechanical Engineering • Industrial and Systems Engineering, or a closely related discipline. Depending on the specific PhD project, desirable technical experience may include one or more of the following: experience in electrical machine design, additive manufacturing and material characterisation, experience in electromagnetic and multi-physics modelling, optimisation techniques for engineering design, experience in prototype development and experimental testing of electrical machines. Candidates should demonstrate a strong interest in interdisciplinary research and an enthusiasm for addressing complex engineering and sustainability challenges. Candidates should be capable of working independently while also contributing effectively to a multidisciplinary research team. Strong analytical, problem-solving, communication and scientific writing skills are essential. As the programme involves international collaborative research and an extended research attachment in Singapore, candidates should also demonstrate adaptability, cultural awareness, effective communication and a willingness to work in an international research environment. Candidates may be required to satisfy the academic, English-language, immigration, security and other eligibility requirements of Coventry University, A*STAR and the Singapore Research Attachment Programme.

资助来源： 学费与奖学金 优势： 成功候选人将获得全面的研究培训，包括技术、个人及职业技能方面的培养。考文垂大学的所有研究人员（从博士到教授）均属于博士学院与研究能力与发展中心，该机构将提供高质量的培训与职业发展支持。

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