Computational containment

The increasing concentration and toxicity of active ingredients in pharmaceutical production are leading to lower and lower OELs. This presents a challenge to the manufacturers of containment technology. It is therefore critical that equipment such as downflow booths provide appropriate flow conditions to carry particulates and vapours away from operators. Traditionally it has not been possible to investigate the effect of design changes or the placement of obstacles and new equipment in a room without physical testing, which is both costly and time consuming. Computational fluid dynamics (CFD) is a simulation technology that offers a cost effective alternative to this.

Accessible CFD CFD is often perceived as a high tech analysis tool requiring large computer resources to be effective. With the ever-increasing performance of the personal computer coupled with modern easy to use interfaces, more and more industries are benefiting from the power of CFD modelling. CFD is essentially a computer-based method for the solution of the fundamental governing equations of fluid dynamics – the continuity (conservation of mass), momentum and energy equations. By solving these equations on a 3D grid representing a physical domain, the prediction of the fluid behaviour is possible. This allows designers and users to predict and visualise the performance of piece of equipment from a single isolator to a complete cleanroom. CFD allows you to model both the airflow and fate of particulates in the room and also obtain information relating to the number of air changers/minute, mean residence time and potential stagnant regions. CFD has been used to investigate in detail the flows in individual containment units such as isolators and laminar flow booths, highlighting causes of poor performance. It is then possible to vary extract locations etc, re-run the model and improve the design before committing to experimental testing and manufacture. The same features are of concern when modelling the complete cleanroom environment, where the appropriate positioning of vents relative to extracts and equipment is critical for obtaining the desired performance. It is also possible to include thermal effects in the model; these could be of interest for both operator comfort but also to ensure that no buoyancy driven flow structures interfere with the rooms operation. A further strength of applying a CFD analysis to a problem is the ability to display the results in ways that deliver complex information in a highly visual and easy-to-understand format, allowing the fluid dynamics at work to be scrutinised quickly and efficiently. This visualisation of results also makes it far easier to quantify and highlight the impact of small design changes made exploratively to the model of the cleanroom ventilation scheme being studied.

Impact of interaction One very recent example of the use of CFD to achieve an effective ventilation system in a cleanroom involved the investigation into the effect of moving and adding equipment such as isolators and LFUs in a space in an existing facility. The interaction between LFUs, isolators and the overall room airflow can have a significant impact both on the unit and the overall room performance. The importance of the location of the various equipment is an element that can be critical to realising an effective system. Various positional arrangements can easily be tested to identify which yield maximum performance. For example, Fig. 1 is a visualisation of the results from the analysis showing path lines from a ceiling vent in the cleanroom. The relative velocity of the airflow is given, colour coded to indicate a range of speeds measured at metres per second. From the results we can clearly see the outer boundaries of the area being successfully ventilated, and how efficient the overall ventilation is. This is just one method of displaying graphically the results of a simulation and highlights the three dimensional nature of the flow structures. It possible to go much further and create such visualisations of final data for a number of chosen variables to ensure all the dynamics at work are fully understood. Fig. 2 shows the age of the air on a slice through the domain. Here, red is used to indicate that air in the region of the centre of the far wall has been present for longer than the surrounding air. It seems clear from the results that, despite the presence of two ventilation units at ceiling level to the right of the far wall as we see it, the position of the tall object left of centre, allied with the bench directly adjacent, has resulted in the occurrence of a recirculation zone in which stale air is becoming trapped rather than effectively removed. This is shown more clearly by Fig. 3 giving the velocity vector plot, where the vectors have been coloured by time. This example demonstrates the ability CFD analysis gives the user to interrogate final data in a variety of formats, allowing the identification of cause suggested in results, but not necessarily immediately obvious. Such a CFD analysis can be conducted either retrospectively to optimise or trouble shoot an existing cleanroom ventilation scheme, or pre-emptively on a potential design for a facility yet to be built. Either way, the result can greatly reduce timescales involved and achieve a corresponding reduction in costs.