The project "CFD Analysis of Mixing Efficiency in High-Viscosity Stirred Vessels", delivered for Unilever PLC in 2022, addressed a practical process and manufacturing challenge through computational fluid dynamics (CFD). Rather than producing simulation images in isolation, the study was organised around the client's design questions: what controlled performance, where the principal risks or losses occurred, and how the design or operating strategy could be improved.

Mixing in stirred vessels is an everyday routine process in almost all food, consumer goods, and pharmaceutical industries. The aim of this project was to predict the flow behaviour using high-fidelity computational simulations within mixing processes in stirred vessels containing miscible liquids with high viscosity ratios. The computational framework developed by our team makes use of accurate multi-phase flow models to predict the mixing time, which is a critical design parameter or assessing the mixing efficiency and is applied to compare the performance of different mixing vessels in industry. An experimental setup was also developed as part of this project using electrical resistance tomography (ERT) technique, providing invaluable data for validating the CFD simulations.

The methodology centred on a high-fidelity multiphase CFD model of miscible, high-viscosity liquids in stirred vessels. The representation retained the impeller, vessel, free surface and evolving concentration field used to determine when effective mixing had been achieved. Boundary and operating conditions covered different viscosity ratios, vessel or impeller arrangements and operating speeds, supported by electrical-resistance-tomography measurements, with material, fluid and equipment properties assigned from the available design information. Resolution was concentrated in regions where steep velocity, thermal, concentration or phase gradients were expected, while the overall model remained efficient enough to compare several credible configurations. This balance allowed system-level performance to be linked to the local mechanisms responsible for it.

The assessment compared mixing-vessel and operating variants relevant to industrial food, consumer-goods and pharmaceutical processes. Performance was judged using mixing time, circulation, concentration uniformity, dead zones, power-related indicators and agreement with the experimental tomography data. Results were reviewed through quantitative summaries and engineering visualisations, such as contours, vectors, streamlines, sections and time histories, selected to suit the physics. Important assumptions and operating uncertainties were considered so that the recommendations relied on repeatable comparative trends rather than a single nominal case.

For Unilever PLC, the principal value was a validated basis for comparing and improving industrial mixing equipment. The final evidence linked each recommendation to the relevant model or process output, making it suitable for internal design reviews, supplier or contractor discussions and, where applicable, planning, safety or regulatory dialogue. The work also created a reusable baseline that can be updated as geometry, operating data or test results become available, reducing the cost and risk of later design iterations.

A disciplined quality-control workflow supported the analysis. Geometry, units, mass and energy balances, boundary-condition consistency and solver convergence were checked before options were ranked. Mesh or parameter sensitivities were used where they were most likely to affect the engineering conclusion, and limitations were documented explicitly. This ensured that the study remained traceable and reproducible rather than relying on isolated simulation images.