The efficient design of irregularly packed fixed-bed reactors requires a deep understanding of the fluid dynamics and heat transfer in the bed. Thanks to recent advances in computational methods, numerical simulations have become an alternative to experimental studies that have been used for the design of random packings in recent decades. However, modeling fluid flow within the bed is challenging because several mechanisms (chemical reactions, mixing, radiation, etc.) occur simultaneously on multiple scales. These mechanisms are strongly influenced by the turbulent structures in the pores between the particles. However, the combination of complex geometric configuration and flows with high Reynolds numbers led to a lack of understanding of the fundamentals of turbulence dynamics and its modeling accuracy in the fixed-bed.
The flow in the fixed-bed exhibits a wide range of temporal and spatial scales, which drastically increases the computational complexity of the modeling. The flow field within the bed exhibits various patterns, including vortex formation, segmentation and reattachment of flow at the surface of the particles, and the formation of flow channels near the outer walls. These structures are influenced by the interaction between the flow and the bed and directly affect the temperature distribution, as thermal energy in a fixed-bed is transported across the entire surface by strong convective flows as the fluid moves through the region. Although the available literature focuses on the accuracy of turbulence modeling and fluid dynamics in spherical packed beds, in industrial applications, packed beds with non-spherical particles are predominant. Moreover, the relationship between the influence of non-spherical particles on turbulence and heat transfer is still unknown.
The goal of this project is to use the data obtained from DNS simulations to improve the accuracy of RANS modeling by estimating the model constants for the flows within the packed bed. In addition, the Proper Orthogonal Decomposition (POD) algorithm will be applied to the flow quantities predicted by the improved RANS model to investigate and quantify the relationship between the global geometric properties of the particles and the dominant flow structures. This provides insight into the coupling between thermohydrodynamics and the geometric configuration of the packed bed.
The FNR project BiNOred is about a development for the realization of very clean working SCR systems (Selective Catalytic Reduction), so that the very strict emission regulations according to the 44th BImSchV (Federal Immission Control Ordinance) from 2023 onwards can be met. These SCR systems are necessary in order to be able to use clean and CO2-neutral biomass in combined heat and power plants (CHP) in the future. For this purpose, a "digital image" for the injection of urea solution into hot exhaust gas is to be developed. The focus is on the parameterization of the influences of the complex relationship between geometric and physical boundary conditions, which differ significantly from those in the automotive and power plant sectors. In particular, the distribution of the urea solution and subsequently of the ammonia released is considered here. In this way, it should be possible to predict the conditions under which the most efficient use of urea solution is possible for specific applications as early as the project planning phase. With the aid of the "digital image", it will then be possible to use urea solution for the operation of an SCR catalytic converter for nitrogen oxide purification of the exhaust gas as a resource that is optimized in terms of demand, while at the same time minimizing ammonia slip. This will also prevent the resulting secondary emissions such as nitrogen oxides or hydrogen cyanide. Unnecessary oversizing of the SCR catalyst volume can thus be avoided. As a consequence, there are essential economic advantages in both the purchase and operation of the SCR system. Only in this way can highly efficient, CO2-neutral biomass utilization be given a real chance, as it is subject to immense cost pressure. The results will be directly utilized in the biogas CHP market, as stricter emission regulations will apply from 2023 due to the entry into force of the 44th BImSchV, which can only be met with an optimally designed SCR system.