Mean age theory in multiphase systems

Russ, David C.; Berson, R. Eric

doi:10.1016/j.ces.2015.10.030

Cited by 12 publications

(18 citation statements)

References 14 publications

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“…It was furthermore applied to the air flow field investigation, MAA and CO 2 distribution, and air-change efficiency when inside a bedroom the heights of the supply air outlet is varied [23], or to particular building ventilation devices such as wind towers and wind-catchers [24,25]. Different applications of similar CFD models, far from the aim of the present paper, were proposed in the chemical and biopharmaceutical fields [26,27] or in the evaluation of the MAA in the outside city ventilation [28].…”

Section: Introductionmentioning

confidence: 99%

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

Buratti

Palladino

2020

Applied Sciences

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The mean age of air (MAA) is one of the most useful parameters in evaluating indoor air quality in natural ventilated buildings. Its evaluation is generally based on the CO2 monitoring within the environment; however, other methods can be found in the literature, but they have not always led to satisfactory results. In this context, the present paper is focused on two main topics: the effect of the windows airtightness and of the environmental conditions on MAA and the application of artificial neural network (ANN) for the CO2 prediction within the room. Two case studies (case study 1 located in Terni and case study 2 located in Perugia) were investigated, which differ in geometric dimensions (useful area, volume, window area) and in airtightness of windows. The indoor and outdoor environmental conditions (air temperature, pressure, relative humidity, air velocity, and indoor CO2 concentration) were monitored in 33 experimental campaigns, in four room configurations: open door-open window (OD-OW); closed door-open window (CD-OW); open door-closed window (OD-CW); closed door-closed window (CD-CW). Tracer decay methodology, according to ISO 16000-8:2007 standard, was compiled during all the experimental campaigns. A feedforward ANN, able to simulate the indoor CO2 concentration within the rooms, was then implemented; the monitored environmental conditions (air temperature, pressure, relative humidity, and air velocity), the geometric dimensions (useful area, volume, window area), and the airtightness of windows were provided as input data, while the CO2 concentration was used as target. In particular, data of 19 experimental campaigns were provided for the training process of the network, while 14 were only used for testing the reliability of ANN. The CO2 concentration predicted by ANN was then used for the MAA calculation in the four room configurations. Experimental results show that MAA of case study 2 is always higher, in all the examined configurations, due to the higher airtightness characteristics of the window and to the higher volume of the room. When the difference between indoor and outdoor temperature increases, the MAA increases too, in almost all the investigated configurations. Finally, the CO2 concentration predicted by ANN was compared with experimental data; results show a good accuracy of the network both in CO2 prediction and in the MAA calculation. The predicted CO2 concentration at the beginning of experimental campaigns (time step 0) always differs less than 2% from experimental data, while a mean percentage difference of −18.8% was found considering the maximum CO2 concentration. The MAA calculated using the predicted CO2 of ANN was greater than the one obtained from experimental data, with a difference in the 0.5–1.3 min range, depending on the configuration. According to the results, the developed ANN can be considered an alternative and valuable tool for a preliminary evaluation of MAA.

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Section: Introductionmentioning

confidence: 99%

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

Buratti

Palladino

2020

Applied Sciences

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“…Conventional residence time theory is a measure of flow distribution at the exit of a continuous system whereas mean age theory provides spatial resolution throughout the interior of the flow domain. Mean age has been demonstrated in a variety of applications for both single phase [62,63] and multiphase flow [66,67]. Multiphase would be beneficial if, for example, one sought to determine the age of blood components such as cells, platelets, and plasma, etc.…”

Section: Residence Time Applications In Cardiovascular Researchmentioning

confidence: 99%

Non-invasive detection and assessment of coronary stenosis from blood mean residence times.

Hashemi¹

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“…Excluding higher-order terms in the Taylor series, an algebraic equation is produced: RTDs to be an operative quantifier of non-ideal mixing in real systems. While useful, a drawback to RTDs is that the produced function is typically limited to discrete sampling at the system exit, thus incognizant of characterizing internal happenings (Russ and Berson, 2016). In effect, an RTD provides no information regarding the spatial distribution of mixing along the length of a system.…”

Section: Clinical Comparison Of Andmentioning

confidence: 99%

“…Consequently, when a molecule reaches the exit, its age equals the residence time. As suggested, complete spatial distributions of age are most valuable, but they are generally unfeasible to develop experimentally since measurement is required across an entire system volume and necessitate tracking the movement of a passive tracer (Russ and Berson, 2016). As a solution, in recent years, the application of mean age theory has provided an innovative approach in research associated with spatially resolved mixing.…”

Section: Clinical Comparison Of Andmentioning

confidence: 99%

“…As a solution, in recent years, the application of mean age theory has provided an innovative approach in research associated with spatially resolved mixing. Mean age theory permits redefining time as a passive scalar in the transient advection-diffusion equation, facilitating analysis of traditionally time-based variables (e.g., mean residence time) while using a steady-state solution (Russ and Berson, 2016). Passive scalars may be thought of as virtual tracers that do not impact flow but are transported by the system as a…”

Section: Clinical Comparison Of Andmentioning

confidence: 99%

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Comparing stenotic blood flow in three- and two-dimensional arterial renderings using computational fluid dynamics and multiphase mean age theory.

Garza¹

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Over one million invasive coronary angiography procedures are performed annually in patients who experience chest pain or are known to have coronary artery disease. The procedure is carried out to ascertain the degree of arterial blockage (stenosis) that hinders blood flow to the heart. A cardiologist performing the procedure determines the physiological degree of a stenosis by either visual estimation, which is routine practice, or by invasively measuring fractional flow reserve (), which is the current gold standard that has been demonstrated to improve patient outcomes and temper the cost of healthcare. Nevertheless, is performed in only 10-20% of patients because it is invasive, expensive, and requires more radiation exposure.

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Mean age theory in multiphase systems

Cited by 12 publications

References 14 publications

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

Mean Age of Air in Natural Ventilated Buildings: Experimental Evaluation and CO2 Prediction by Artificial Neural Networks

Non-invasive detection and assessment of coronary stenosis from blood mean residence times.

Comparing stenotic blood flow in three- and two-dimensional arterial renderings using computational fluid dynamics and multiphase mean age theory.

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