Next generation turbomachinery with two-phase flow (Training42Phase)

能源系统的绿色转型要求实施许多基于两相流的新型能量转换技术。然而，目前的知识水平和理解的两相流在埃塞俄比亚是在一个非常低的水平，迫切需要教育的科学家，可以支持这种技术的发展。Training 42阶段的目的是用两相流教育未来的领先科学家，从而为开发下一代生物技术提供所需的科学和技术基础。该项目联盟包括解决这些具有挑战性的任务所需的所有关键合作伙伴，并为我们的10名博士候选人提供解决这些挑战的最佳基础-领先的欧洲大学，以及来自大型国际公司和中小企业的工业合作伙伴，代表技术供应商和数字化解决方案提供商。Training 42阶段将为以下领域的10名博士生提供首次优秀培训：1）创新的技术解决方案，使设计具有成本效益和可靠性的两相流换热器成为可能。2)在与两相流涡轮机的建模，设计，优化和性能预测相关的所有领域的尖端多学科培训，包括气动热建模和设计，应力和疲劳分析，结构动力学，混合/数据驱动的故障机制模型，先进的优化方法和基于机器学习的数据驱动的分析技术。3)应用两相流的微扰技术于新兴的新技术。4)可转移技能的顶级培训，如研究和数据管理，科学诚信和道德，知识产权和创业精神，商业规划，职业规划和赠款提案撰写。

The green transition of energy systems requires the implementation of a number of novel emerging energy conversion technologies based on turbomachinery with two-phase flow. However, the current level of knowledge and understanding of two-phase flow in turbomachinery is at a very low level, urgently calling for the need of education of scientists that can support the development of such technologies. The aim of Training42Phase is to educate the future leading scientists within turbomachinery with two-phase flow, thereby providing the scientific and technological basis required for the development of the next generation of turbomachinery. The project consortium includes all the key partners needed to address these challenging tasks and provide our group of 10 doctoral candidates with the best foundation for solving these challenges - leading European universities, and industrial partners from large international companies and SMEs, representing technology suppliers and providers of digitalized solutions. Training42phase will provide a first of its kind excellent training for 10 doctoral candidates within the following fields: 1) Innovative technological solutions that enable the design of cost-effective and reliable turbomachinery with two-phase flow. 2) Cutting-edge multi-disciplinary training in all fields related to the modelling, design, optimization and performance prediction of turbomachinery with two-phase flow, including aero-thermal modelling and design, stress and fatigue analysis, structural dynamics, hybrid/data-driven failure mechanism models, advanced optimization methods and data-driven analytic techniques based on machine learning. 3) Application of turbomachinery with two-phase flow to emerging novel technologies. 4) Top-level training in transferable skills, such as research and data management, scientific integrity and ethics, intellectual property rights and entrepreneurship, business planning, career planning and grant proposal writing.