The XDEM Multi-physics and Multi-scale Simulation Technology: Review on DEM-CFD Coupling, Methodology and Engineering Applications

The XDEM multi-physics and multi-scale simulation platform roots in the Ex- tended Discrete Element Method (XDEM) and is being developed at the In- stitute of Computational Engineering at the University of Luxembourg. The platform is an advanced multi- physics simulation technology that combines flexibility and versatility to establish the next generation of multi-physics and multi-scale simulation tools. For this purpose the simulation framework relies on coupling various predictive tools based on both an Eulerian and Lagrangian approach. Eulerian approaches represent the wide field of continuum models while the Lagrange approach is perfectly suited to characterise discrete phases. Thus, continuum models include classical simulation tools such as Computa- tional Fluid Dynamics (CFD) or Finite Element Analysis (FEA) while an ex- tended configuration of the classical Discrete Element Method (DEM) addresses the discrete e.g. particulate phase. Apart from predicting the trajectories of individual particles, XDEM extends the application to estimating the thermo- dynamic state of each particle by advanced and optimised algorithms. The thermodynamic state may include temperature and species distributions due to chemical reaction and external heat sources. Hence, coupling these extended features with either CFD or FEA opens up a wide range of applications as diverse as pharmaceutical industry e.g. drug production, agriculture food and processing industry, mining, construction and agricultural machinery, metals manufacturing, energy production and systems biology

The eXtended Discrete Element Method (XDEM): An Advanced Approach to Model Blast Furnace

The blast furnace iron making is the oldest but still the main method to produce liquid iron through sequential reduction processes of iron ore materials. Despite the existence of several discrete and continuous numerical models, there is no global method to provide detailed information about the processes inside the furnaces. The extended discrete element method known as XDEM is an advance numerical tool based on Eulerian–Lagrangian framework which is able to cover more information about the blast furnace process. Within this platform, the continuous phases such as gas and liquid phases are coupled to the discrete entities such as coke and iron ore particles through mass, momentum and energy exchange. This method has been applied to the shaft, cohesive zone, dripping zone and hearth of the blast furnace. In this chapter, the mathematical and numerical methods implemented in the XDEM method are described, and the results are discussed

Application of heat pumps and thermal storage systems for improved control and performance of microgrids

The high penetration of renewable energy sources (RES), in particular, the rooftop photovoltaic (PV) systems in power systems, causes rapid ramps in power generation to supply load during peak-load periods. Residential and commercial buildings have considerable potential for providing load exibility by exploiting energy-e_cient devices like ground source heat pump (GSHP). The proper integration of PV systems with the GSHP could reduce power demand from demand-side. This research provides a practical attempt to integrate PV systems and GSHPs e_ectively into buildings and the grid. The multi-directional approach in this work requires an optimal control strategy to reduce energy cost and provide an opportunity for power trade-o_ or feed-in in the electricity market. In this study, some optimal control models are developed to overcome both the operational and technical constraints of demand-side management (DSM) and for optimum integration of RES. This research focuses on the development of an optimal real-time thermal energy management system for smart homes to respond to DR for peak-load shifting. The intention is to manage the operation of a GSHP to produce the desired amount of thermal energy by controlling the volume and temperature of the stored water in the thermal energy storage (TES) while optimising the operation of the heat distributors to control indoor temperature. This thesis proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), and thermal and electrical energy storage systems. The results of this research demonstrate to rooftop PV system owners that investment in combined TSS and battery can be more profitable as this system can minimise life cycle costs. This thesis also presents an analysis of the potential impact of residential HP systems into reserve capacity market. This research presents a business aggregate model for controlling residential HPs (RHPs) of a group of houses that energy aggregators can utilise to earn capacity credits. A control strategy is proposed based on a dynamic aggregate RHPs coupled with TES model and predicting trading intervals capacity requirements through forecasting demand and non-scheduled generation. RHPs coupled with TES are optimised to provide DSM reserve capacity. A rebound effect reduction method is proposed that reduces the peak rebound RHPs power