Data driven insights into oxygen pressure surge testing

This abstract presents a comprehensive study on oxygen pressure surge testing (OPST)
in Mechanical Engineering, focusing on the critical role of data analytics and computational fluid dynamics (CFD) in enhancing safety and reliability. OPST, a vital procedure governed by the ISO 10297 standard, evaluates the performance of oxygen systems under high-pressure conditions.
In our research, we employ advanced data analytics techniques to analyze test data
collected during OPST experiments up to 100 bar. Leveraging large volumes of test data, we extract valuable insights into flow behavior and system dynamics. This data-driven approach
enables us to identify patterns, anomalies, and critical parameters that influence system safety.
Furthermore, we utilize computational fluid dynamics (CFD) simulations, conducted
using Ansys Fluent on a supercomputer cluster, to complement experimental findings. By integrating CFD with test data analytics, we gain a deeper understanding of flow behavior during OPST, particularly focusing on adiabatic compression effects and their implications for system safety.
Through meticulous analysis of CFD results and test data, we uncover nuanced insights
into pressure-temperature surges, supersonic flow characteristics, and pressure profile
variations. These insights play a pivotal role in informing engineers during the design phase, enabling them to develop safer and more reliable products.
Overall, our research underscores the importance of data-driven approaches in
Mechanical Engineering, demonstrating how the fusion of test data analytics and CFD
simulations enhances our understanding of complex phenomena like OPST. By leveraging these insights, engineers can make informed decisions and design oxygen systems that meet stringent safety standards and regulatory requirements.