Placement Optimization for Air Purification Devices in Large Indoor Spaces copy

An in-depth literature review was conducted to understand the work done in this field, and to identify an understudied area of interest. Whilst many studies explored contaminant and airflow modelling independently, few looked at optimising ventilation under both conditions, in environments containing obstacles. Understanding airflow in such environments was essential, as insufficient air distribution could lead to the formation of stagnant and pollutant-heavy air pockets, degrading the overall indoor air quality. Conflicting research on the recommended direction of ventilation to control contaminant flow highlighted a critical need to explore this area further, to effectively cleanse rooms with minimal cross-contamination. This was be a crucial factor in achieving safe purification in large – and often shared – spaces. Hence, there was a strong, justified and timely opportunity to explore the effects and optimisation of device placement to achieve both effective and safe air flow in populated indoor environments.

To understand the impact and significance of placement positioning of an air purification device for a given environment geometry and ventilation system.

Given that this was a fluid modelling and optimisation problem, Computational Fluid Dynamics (CFD) was used to achieve the primary objective. This ensured a realistic and feasible solution, given the short timeframe and remote working limitations. Studies have validated CFD simulations of contaminant distributions against experimental results, highlighting this approach to be feasible and well-supported by existing research.

An office environment was modelled, with 12 occupants, 12 computers and 4 printers as contaminant sources, and 12 desks as bluff bodies. This set up was adopted from a comparable study which had been numerically and experimentally validated.

Four scenarios in this office environment were modelled, with the first three looking at different placement of a purifier to understand how placement impacted air quality. In the fourth scenario, the modelled filter was replaced with a stronger one, to understand the comparative effects of placement and filter strength on the final air quality results.

Existing literature was used to determine the correct settings to implement into the numerical solver of the CFD program. A single pollutant was modelled (due to limitations in the software allowing modelling of only two fluids) through combining the individual properties of all the pollutants expected to be present in an office environment.

A Blueair Pro XL purifier was selected to be modelled, due to its stability within the market, as well as its verified and easily available specifications. In Scenarios 1-3, a HEPA 12 filter is modelled, with a stated removal efficiency of 99.50%. Scenario 4 utilises a HEPA 14 filter, with an efficiency of 99.995%.

Interestingly, there was significant fluid separation in all three initial scenarios, with the denser pollutant sinking to the ground to form a thick concentrated blanket. This layer was important to consider because any disruptions to it would form a cloud of highly concentrated and hazardous pollutant.

The importance of this was seen in Scenario 1, where the purifier being placed parallel to the direction of ventilation did not reduce the velocity of the air coming out of the purifier, contrary to that in Scenario 2 where opposing velocities minimised the final velocity out of the purifier. This higher comparative velocity in Scenario 1 led to the formation of a recirculation zone at the back wall, significantly disrupting the pollutant blanket. Comparing the results (at the occupant breathing zone) in all three scenarios, Scenario 1 showed more occupants breathing in hazardous regions of fluid. The recirculation zone also caused the nearby occupant to feel uncomfortably cold, and this is important as studies have highlighted that excessive cooling aggravates symptoms of the Sick Building Syndrome.

Stagnant zones were also evident in all three scenarios. The differing results are put down to increased purifier velocity in Scenario 1, where the Coanda effect was more prominent, with air travelling along the walls and reaching further, dispersing more of these stagnant zones.

These results, along with a few others, highlighted the significant effects of purifier placement. Thus, it was decided that a new purifier efficiency metric would be designed, considering the wider-system performance instead of just filter strength. The final formula developed compared the overall pollutant removed by the purifier – accounting for filter strength – against the level of pollution in the breathing zone, as reducing pollution in this region is paramount in ensuring safety of the occupants in the room.

Pulling the necessary results from each scenario, a purifier efficiency rating was calculated. Interestingly, Scenario 2 had the highest efficiency rating, followed by Scenario 4. (Reminder: The purifier in Scenario 4 has the improved filter and is placed in a different location to the one in Scenario 2.) Even though Scenario 2 had a higher room concentration and a poorer performing filter, the concentration in the breathing zone was very similar to that of Scenario 4. This example highlights the need to consider not only filtration efficiency, but also the placement of the purifier.

Purifier Design:

Using the results from this project’s case study, the highly concentrated pollutant blanket across the floor could be easily disturbed and push pollutants towards breathing zones. It is suggested that in use-cases where such fluid separation is seen, floor-based purifier designs should be used, to extract pollutants from the ground and expel purified air through the top of the device.

Environment-specific Product Performance Rating:

Use-case adaptations of the proposed model and Purifier Efficiency metric could be used to design a new set of standards and assess purifier performance as part of a larger system. For example, models of a small office space and hotel lobby could be simulated to extract the necessary results to perform Purifier Efficiency calculations for each environment. These ratings could inform consumer decisions when selecting purifiers for specific use-cases.

The above can be implemented in industry, through the creation of an advisory tool for manufacturers and end-consumers. Environment parameters within the proposed model proposed could be altered to adapt to different use-cases through a simplified digital interface and outwards-facing API. Numerous model iterations could be simulated using a cloud-solver, to identify optimal placements and filter strengths for a specified purifier in the modelled environment. This could be easily achieved via SimScale, an existing 3rd party software, using their API and cloudsolving capabilities. However, a significant cost is associated with the use of the platform, thus it was not explored in this project. Should these commercial impediments be overcome, the model proposed within this study is validated for application and can be readily packaged into a commercial tool.

Discussions were held with directors at Blueair, a leading manufacturer in the field. They showed a great interest in my area of work, echoing that purifiers should be defined by more than just their filter quality, and that instead we should be looking at the wider system they sit in.

They also mentioned the difficulties faced when trying to justify to consumers that they often don’t need industry-grade filters for their specific uses. Hence why the company has strongly supported the work done to date, as it forms the basis of an unprecedented commercial tool to prove that a purifier in an office, for example, can perform strongly without the need for a surgical-grade filter, and can be further optimised just by considering its placement.