(88d) Rapid Prediction of Mixing Characteristics – an Enterprise Solution

Traditionally, computational fluid dynamics (CFD) has been primarily utilized by subject matter experts (SMEs) to predict mixing behavior in complex mixing systems. However, as pharmaceutical companies accelerate their digital transformation, there is a growing need to make advanced modeling tools accessible to a broader range of users across the organization. This mindset shift requires the integration of design of experiments (DoE), CFD, and machine learning or reduced-order modeling (ML/ROM) within user-friendly, app-based platforms.

This study presents the development and deployment of such an integrated solution, designed to democratize the use of advanced modeling techniques. The proposed framework enables rapid, prediction of mixing characteristics, facilitating informed decision-making beyond traditional SME boundaries. Results highlight how this approach supports the scalability and speed required for modern pharmaceutical drug development.