Lightweighting using multi-materials and parts integration

汽车行业正面临着下一代汽车设计的重大挑战，因为燃油经济性法规和尾气排放标准继续努力提高效率。为了确保车辆设计达到这些可持续发展目标，通过多材料设计和拓扑优化的轻量化被认为是减轻传统底盘和车身结构重量的主要方法。现在需要更有效的工具、技术和方法来推动多阶段优化工具的开发，使其超出当前的商业能力，并帮助汽车设计人员在不牺牲性能的情况下实现关键的效率改进。这项研究将完成三个主要任务，这是现有商业软件无法完成的：(1)多材料拓扑优化(考虑层状、各向异性材料)和NVH(噪声、振动和粗糙度)；(2)多关节拓扑优化和零件固结优化；(3)完全集成到多材料拓扑优化和多关节拓扑优化的大规模拓扑优化。所有这三项任务都寻求解决多材料设计的下一个主要挑战，并建立在该领域最先进的研究基础上，目前正在女王大学进行。这项研究的主要目标是创造先进的多材料设计算法，开发新的计算工具，并为下一代汽车设计解决复杂的、现实世界的行业问题。该项目将在整个研究活动期间为4名博士生和6名硕士学生提供支持，直至毕业。

The automotive industry is facing significant challenges for next-generation vehicle design as fuel economy regulations and tailpipe emission standards continue to strive for greater efficiency. In order to ensure vehicle design reaches these sustainability targets, lightweighting through multi-material design and topology optimization has been suggested as the leading method to reduce weight from conventional chassis and body structures. More effective tools, techniques, and methodologies are now required to advance the development of multi-phase optimization tools beyond current commercial capability, and help automotive designers achieve critical efficiency improvements without sacrificing performance. The proposed research will perform three main tasks, which cannot be achieved by means of currently available commercial software: (1) multi-material topology optimization for composites (by considering layered, anisotropic materials) and NVH (noise, vibration, and harshness); (2) multi-joint topology optimization and optimization for parts consolidation; and (3) large-scale topology optimization with full integration onto multi-material topology optimization and multi-joint topology optimization. All three tasks seek to address the next major challenges in multi-material design and build upon the state-of-the-art research in the field, currently being conducted at Queen's University. The primary objectives of this research are to create advanced multi-material design algorithms, develop new computational tools, and solve complex, real-world industry problems for next-generation vehicle design. The project will support total 4 PhD students and 6 MSc students throughout their research activities and until graduation.