In recent years, numerical simulation has emerged as a robust tool for the analysis of physiological phenomena. The application of computational fluid dynamics (CFD) techniques to the study of biofluids is a constantly growing field, especially the focus given to simulations of blood through the circulatory system and air within the human airways. A high complexity arises in the analysis of these systems. On the one hand, the extension and configuration of the geometrical model (branches, networks), and on the other hand, the multiphysics nature of many of these phenomena. This research work was developed with the aim of exploring methodologies that help to simplify the complexity of simulations associated with biofluids, particularly in human airways. In the first part, a specification of the basic concepts was developed, focusing on the description of the airways and the fluid dynamics associated with air transport in the respiratory system. In turn, a background of numerical simulation applied to biofluids, and a classification of the hybrid simulation methodologies was discussed. In the second part, a first simplification strategy was studied, specifically the use of synthetic airway models. For this purpose, a comparison study of the use of these models vs real patient-specific models was carried out. In addition, a study of the effect of the variation of some morphological parameters on the flow, such as bifurcation angle and carina radius rounding, was developed. In the third part, the implementation and validation of a hybrid simulation methodology was performed, based on a dimensional reduction from the airway homothety ratios. A boundary condition for the pressure, which is the result of this methodology, was implemented in a open source, and tested with two application cases: a study of airways in asthma condition and a study of branch collapse. Finally, general conclusions about the application of the spatial simplification strategy and the use of the hybrid simulation methodology were detailed, as well as recommendations and future work.