# Development and Evaluation of a Simplified CFD Model for a Swirl Diffuser with the Aid of 2D Particle Image Velocimetry

## TL;DR

This paper presents a simplified CFD model for a swirl diffuser validated using PIV measurements, aiming to reduce simulation costs while maintaining accuracy.

## Contribution

The study introduces a corrected simplified model using momentum adjustments and the SST k-ω turbulence model for efficient and reliable simulations.

## Key findings

- A simplified model with adjusted momentums improved accuracy compared to geometry-based models.
- The SST k-ω turbulence model provided a good balance between accuracy and computational efficiency.
- PIV measurements were effective for validating and refining simplified CFD models despite dimensional limitations.

## Abstract

In numerical modeling of swirl diffusers, simplifying
the diffuser
geometry can substantially reduce simulation costs. This study describes
the development and performance evaluation of simplified computational
fluid dynamics (CFD) models of a swirl diffuser by using 2D particle
image velocimetry (PIV). Two simplified models were created by replacing
the diffuser with simple supply openings, where momentums were prescribed.
The first model defined momentums solely based on geometric rules.
In the second, it was demonstrated how the accuracy of the first,
geometry-based model, can be improved by adjusting the momentums using
results from a detailed CFD model. The effect of using the Reynolds
stress model or the SST k-ω turbulence model
on the accuracy of the CFD model and computation time was investigated.
The simplified models were benchmarked against both the detailed CFD
model and the PIV measurements. The corrected simplified geometry
combined with the SST k-ω turbulence model
achieved a favorable balance between reliability and computational
efficiency. Although the two-dimensional nature of the PIV measurements
presented challenges for three-dimensional CFD validation, they proved
valuable for developing and assessing the simplified models.