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Reducing energy demand
Published in Jane Powell, Jennifer Monahan, Chris Foulds, Building Futures, 2015
Jane Powell, Jennifer Monahan, Chris Foulds
An airtightness test provides a crucial measure of the airtightness of a building, indicating the location and extent of uncontrolled air paths. In an airtightness test all vents, windows and doors are closed and a fan unit is used to pressurise and depressurise the building. This allows the measurement of the amount of air that leaks into and out of the building and therefore quantifies the amount of draughts that may exist. The location of the leakages can be identified by the use of a smoke ‘pencil’, with the smoke showing the air movement. The results of an airtightness test can be expressed in two ways: Air permeability – rates are measured in cubic metres of air leaking through a metre square of external fabric per hour at a pressure difference of 50 Pa (with units m3/hr/m2 of total surface area).Air changes per hour (ach) – measured as the number of air changes occurring per hour from a building of a given volume.
Computational Fluid Dynamics
Published in Ulrike Passe, Francine Battaglia, Designing Spaces for Natural Ventilation, 2015
Ulrike Passe, Francine Battaglia
Using the CFD results to calculate the velocity through the left opening (Figure 12.5), the corresponding Reynolds number is 23,090. The Grashof number is Gr = 3.17 × 109. The flow presented in Figure 12.5 corresponds to a buoyancy-dominated flow, where Gr/Re2 = 6, and confirms that the flow velocity is very low into the room. To quantify the ventilation, the air changes per hour (ACH) is used. ACH is defined as the volume flow rate (m3/hr) per volume of the room (m3) and gives a value of how often the air changes per hour. The ACH for this flow is estimated to be 1. While this is acceptable and exceeds the minimum ASHRAE23 recommendation of 0.35, it is below the Royal Institute of British Architects (RIBA)24 recommendation of 2 to 15 to remove heat for cross-ventilation. The ACH values for cross-ventilation were shown in Table 7.2 for the wind-driven flows (Figure 7.11). These values ranged from 12 to 240. In contrast, the buoyancy-driven flows (Figure 12.4) contributed to an ACH of 1.
Design and Qualification of Controlled Environments
Published in James Agalloco, Phil DeSantis, Anthony Grilli, Anthony Pavell, Handbook of Validation in Pharmaceutical Processes, 2021
Franco De Vecchi, Phil DeSantis
Clean rooms employ various designs, including filtration, to achieve airborne particulate limits. Among these are increased air flow (air changes per hour—ACH), specialized air diffusers, ceiling HEPA coverage, and type and location of air return registers. All of these have an influence on air cleanliness. There are no specific requirements for any of these, only guidelines and good engineering practices that designers use to satisfy requirements. It is a myth that any specific number of air changes (e.g., 20 ACH) is a requirement for any clean room. Even when these are referred to in the various guidelines [19], this is a guidance number that may or may not be acceptable based on the other important design criteria cited.
A Lagrangian Approach Towards Quantitative Analysis of Flow-mediated Infection Transmission in Indoor Spaces with Application to SARS-COV-2
Published in International Journal of Computational Fluid Dynamics, 2021
Joseph Wilson, Shelly Miller, Debanjan Mukherjee
For our case study, we select a simple model of a single room – dimensions ; with a single door (closed), a single window (effective air ventilation area of 0.03125 m2); and no furniture or other space modifications. The room size leads to around 173 sq.ft. floor area, which is used in commercial real estate estimates as equivalent to a small office space or meeting room. The room was supplied with one inflow and one exhaust vent of dimensions . Ventilation was characterised using Air Changes per Hour or ACH, a measure of the number of times the air within a defined space is replaced in one hour. The ventilation flow rate was adjusted to achieve an ACH of 3.0, which is in the range of American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) recommendation for occupied public offices. Six different combinations of inflow and exhaust vent layouts were modelled by only changing the locations of the vents (see Figure 1). Three of them have inlet and exhaust located on opposite walls (models: 180UU, 180UL, 180LL respectively in Figure 1), and the remaining have inlet and exhaust located on perpendicular walls (models: 90UU, 90UL and 90LL respectively in Figure 1). Within the room, five different infected hosts or index patients were considered, and occupancy patterns were imposed for other non-infected subjects for whom we estimated . Varying levels of respiratory particle transport were considered by varying effective particle diffusivity in flow.
A methodology for quantifying flow patterns in a water-table apparatus for naturally ventilated buildings
Published in Architectural Science Review, 2021
Pooja Mundhe, Rashmin M. Damle
Air changes per hour (ACH) is defined as the number of times the air in the room is renovated. The higher the ACH value, the higher is the ventilation in the room. Sherman (1990) reported a review of different tracer gas techniques for measuring ACH. Cheng and Li (2014) reported a method for the calculation of ACH which used dry ice as a source of CO2 generation in the room. Persily (2016) highlighted the importance of the relationship between IAQ and ACH, which impacts both the indoor environment and the energy consumption of the building. Dockery and Spengler (1981) proposed a model based on the conservation of mass for determining ACH; the model is represented by equation (4). This equation, which considers the inside and outside concentrations over the evaluation period, is used in the present study as well. In the present study, it was assumed that all the particles entered the room without any loss at the window, so P = 1. The S/V ratio was zero since there was no source, and k was zero as there was no reaction between the dye and the model walls. The outdoor concentration (Cs) was calculated by averaging the concentration values just outside the window, while the indoor concentration (Ci) was the average concentration in the room as depicted in Figure 5a. Using the equation (4), the inside concentration values were predicted on the basis of the outdoor concentration values, by varying the ACH to minimize the sum of the square of differences, i.e. chi (χ2), between the experimental and the predicted indoor concentration values, as illustrated in Figure 5b. The final average value of ACH was such that it minimized the chi (χ2) value in a way that the experimental and the predicted indoor concentration values came as close as possible. The Solver in Microsoft Excel (Microsoft Excel 2016) was used for this optimization.
Survey of particle production rates from process activities in pharmaceutical and biological cleanrooms
Published in Science and Technology for the Built Environment, 2019
Oluwaseyi T. Ogunsola, Junke Wang, Li Song
The methods used to achieve required cleanliness make cleanrooms energy-intensive due to requirements for high airflow rates and system static pressures. Their process requirements additionally lead to high cooling loads (Mathew et al. 2010). Several studies have shown that facility managers have no awareness of how energy efficient their cleanroom facilities are, relative to similar cleanroom facilities of the same cleanliness and classification (Tschudi et al. 2001; Xu and Tschudi 2002). Tschudi et al. (2001) described the scale and proportion of industries utilizing cleanrooms in California and indicated that owners and operators had little information concerning where to place their resources to improve efficiency due to high energy costs. The researchers pointed out that the facility managers just use simple comparisons of energy per floor area and watts per unit of product. These methods all focused on overall production efficiency but overlooked the efficiency (and opportunities for improvement) of energy-intensive environmental or process systems. Consequently, the study presented a benchmarking strategy for use in cleanrooms to obtain energy end use breakdown. Similarly, Mathew et al. (2010) and Xu and Tschudi (2001) presented key metrics and benchmarks to enable facility managers to assess and track cleanroom energy efficiency. The system level metrics of air change rates and energy index of air handling system (W/cfm or W/m3/s) are part of the identified metrics. The study also showed that there is a need to optimize cleanroom air change rates to satisfy the required cleanliness levels. Higher air changes per hour (ACH) than appropriate would lead to unnecessary energy use. The benchmarking results showed that ACH varied significantly among different cleanrooms with the same cleanliness classifications. In real cleanroom operations, there are no explicit quantitative metrics and targets for meeting energy efficiency goals. The study represented the first opportunity to minimize cleanroom energy use as identification and optimization of operating parameters based on actual process requirements within the cleanroom.