# A Systematic Review of Computational Fluid Dynamics Models in the Stomach and Small Intestine
## 4 Discussion and Future Directions  
This systematic review has identified 15 stomach and 15 small intestine articles that have used CFD modeling to assess fluid flow patterns, mixing, and digestion processes within the respective organs. Characteristics of these studies including the numerical techniques, the type of geometry used, and the approaches used to simulate motility were compared. Overall, there has been significant progress in the development of CFD models of the stomach and small intestine. These models provide a unique insight into the complex fluid dynamics and transport phenomena occurring in the GI tract, which cannot be easily  
Figure 7: CFD model of the small intestine developed by [PERSON] et al. [71] using a coupled SPH-DEM modeling framework. Velocity contours of the liquid phase (**left**) and motion of the solid phase (**right**) at three different points in time. Negative values of Vz indicate reversed flow present under contracting regions. Adapted with permission from [PERSON] et al., Applied Mathematical Modelling; published by Elsevier, 2017.  
studied in vivo or in vitro. However, despite the progress made, there are still numerous challenges that need to be tackled. The lack of subject-specific stomach geometries and deformation patterns has been a major limitation in previous CFD models of stomach motility. Nearly all studies have used the MRI data from [PERSON] et al. [34] as a basis for their models, which may not accurately represent the anatomical and physiological variability between different individuals. However, recent developments in MR imaging has led to the creation of subject-specific CFD models of gastric motility [73], such as the one presented in Figure 8. These models allow for a more accurate representation of individual variation and could lead to more personalized diagnoses and treatments for GI disorders. To accurately simulate the digestive processes within the stomach, it is also essential to model the breakdown of solid foods. Most current models only focus on the fluid phase, or even treat the boluses as a fluid, which can lead to inaccurate results. Accurate modeling of solid food digestion will require coupling different numerical techniques to simulate the complex interactions between solid and fluid phases. Additional experiments are required to gather data on the mechanical and chemical properties of different solid foods that have undergone mastication. Such developments will likely lead to more realistic simulations of the digestive process and enable researchers to better understand the effects of different food properties and compositions on the digestive process. The ability to compute gastric flow and mixing patterns over longer periods of time (\(\mathcal{O}(1000)\)\(s\)) is crucial for predicting gastric breakdown and digestion. Digestive processes in the stomach takes several hours and it is essential to capture the temporal evolution of the flow and mixing patterns to derive meaningful results. However, current limitations in computational power make it challenging to perform such simulations over longer periods of time. [PERSON] et al. [31] highlights the computational expenses associated with their model which required \(\sim\)6000 h of CPU time to simulate a cycle of 20 s. They were able to significantly reduce the computational cost by taking advantage of the periodicity of the flow patterns i.e., the flow fields from the third cycle (after 40 s) were repeated, and only the transport equations for the chemical species were solved. This enabled simulation of digestion over 5000 s, therefore future studies with periodic deformation patterns should consider a similar approach. This would not work for the simulation of solid food digestion  
Figure 8: Subject-specific CFD model of gastric motility developed by [PERSON] et al. [73]: (**a**) MR image of gastric peristalsis, (**b**) resulting flow patterns visualized with velocity vectors and (**c**) spatial variation in gastric mixing (after 50 s) visualized using particle tracers.