Incorporating additional multiphysics aspects like radiation, soot formation, or heterogeneous reactions into the nekCRF solver presents several challenges and trade-offs. One major challenge is the increased computational complexity associated with these phenomena. For instance, radiation modeling often requires solving additional transport equations, which can significantly increase the computational load and memory requirements. This necessitates careful consideration of the numerical methods employed to ensure stability and accuracy, particularly in high-temperature environments where radiation effects are pronounced.
Another challenge is the coupling of different physical processes. For example, the interaction between fluid dynamics, chemical reactions, and radiation can lead to complex feedback mechanisms that are difficult to model accurately. This requires robust algorithms capable of handling the non-linear interactions between these processes, which may complicate the solver's implementation and increase the potential for numerical instability.
Trade-offs also arise in terms of simulation fidelity versus computational efficiency. While incorporating detailed models for soot formation or heterogeneous reactions can improve the accuracy of predictions, it may also lead to longer simulation times and higher resource consumption. Therefore, a balance must be struck between the level of detail in the multiphysics models and the available computational resources, particularly in the context of exascale computing where efficiency is paramount.