The research group Energy Technology and Fluid Dynamics
consists of some 70 persons comprising faculty, support staff and PhD students. We are part of the Department of Mechanical Engineering of Eindhoven University of Technology in the Netherlands. The mission our group is to advance heat & flow technologies for energy and high-tech applications. This mission encompasses research and development of new methods and tools (science); improving applied systems (technology); exchanging knowledge with our partners in society (valorization) and teaching and inspiring the future generation of engineers (education).
The scientific and technological challenges in the areas of energy technology and high-tech systems are huge. In order to deal with the challenges, both fundamental and application-oriented developments are necessary. If there is one single discipline where you really can make a difference as an engineer or scientist, it is in the field of energy technology & fluid dynamics. One of today's most prominent societal challenges is the energy transition. Within the upcoming years our society will have to meet stringent CO2 emission targets in order to mitigate climate change. We believe that the solution largely lies in improved heat storage technologies. The topic of the open PhD position is the performance enhancement of packed bed medium-temperature thermal energy storage using a multi-scale methodology approach.The present project is performed in close collaboration with VITO, the Flemish Institute for Technological Research, Belgium. Half of the research work will be performed at VITO, EnergyVille location in Genk. More information about VITO and EnergyVille can be found on an
d www.energyville.be/en, respectively. The selected PhD-candidates will have interviews at both TU/e and VITO.Background
Industry accounts for 23% of greenhouse gas emissions globally (IPCC 2014). Many processes in chemical and manufacturing plants include thermally-driven batch steps. Thermal energy storage, packed bed technology in particular, has the potential to play an important role in making industrial processes more energy efficient and in reducing greenhouse gas emissions. Next to industrial environments, packed-bed storage can also be used to decouple supply and demand in systems relying on medium-temperature intermittent renewable energy sources, like concentrated solar thermal systems (CST).Problem description
Presently, at medium-temperatures levels between 100 °C and 400 °C, thermal storage is mostly done using expensive thermal oil storage or packed-bed storage systems, which use the thermal capacity of a cheap solid-state filler material to store thermal energy by interaction with a heat-transfer medium like thermal oil, steam or hot air. The most common filler material used is natural gravel, which is irregularly shaped. This has two distinct consequences. Firstly, due to the irregular shape, the flow throughout the packed-bed storage will mainly follow the path of least resistance, leading to preferred routes and dead zones in the storage unit. Some parts of the thermal storage unit will hence not be (optimally) used, resulting in reduction of overall performance. Secondly, the size of the filler materials is in general small, resulting in high pressure drops over the storage unit, which requires high power consumption by the circulation pumps thus further reducing the storage efficiency.
Overcoming these barriers is the subject of the present PhD topic. It is the ambition to bundle the expertise available at both TU/e and VITO to develop a methodology and toolset that are capable to optimize the design of a packed-bed storage at different levels.Tasks
At material scale, the optimal filler shape will be investigated using shape optimization methods, considering requirements imposed by the processes, like charging and discharging power, maximum allowed pressure drop and heat-transfer medium. In particular, the adjoint-based method will be considered as an option for shape optimization, which is also being used in other projects within VITO. As an alternative, traditional approaches based on Computational Fluid Dynamics (CFD) will also be investigated. Candidate materials will be inventoried, and, if needed, characterized using thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) available at TU/e. The production process scalability and sustainability of the candidate materials will be highlighted, focusing on the use of waste or recycled materials such as slag, potentially in collaboration with SuMAT at VITO.
At reactor level, numerical models will be developed to investigate the optimal reactor design and its performance. This can be done using detailed CFD modelling as well as reducedorder models constructed from CFD models and data-based models identified from experimental or CFD data (TU/e) that are suitable for optimization problems. A small-scale prototype reactor can be constructed to validate the numerical methodology and validate it with experiments that can be performed either at VITO or at TU/e, depending on the required experimental conditions.
At system scale, the development of a model that can be integrated in a broader system simulation, with a low computational cost, will be investigated. The aim is to assess the performance of the thermal storage coupled with the other system components, following a control strategy. The system-level model can be constructed from the abovementioned reduced-order models or data-based grey-box/black-box models to be developed at the reactor level.
The activities in this PhD topic proposal combine expertise available in VITO (adjoint-based optimization, CFD modelling, SuMAT & lab infrastructure) and TU/e (packed-bed storage expertise, CFD and DNS modelling of porous media, model reduction techniques, lab infrastructure & material characterization equipment). The outcome of the project should eventually result in a methodology and toolset that is capable to design packed-bed thermal storage systems in an optimal and economically viable way, considering the requirements of the process.