Numerical Modeling of Turbulent and Laminar Airflow and Odorant Transport during Sniffing in the Human and Rat Nose

Abstract

Human sniffing behavior usually involves bouts of short, high flow rate inhalation (>300 ml/s through each nostril) with mostly turbulent airflow. This has often been characterized as a factor enabling higher amounts of odorant to deposit onto olfactory mucosa than for laminar airflow and thereby aid in olfactory detection. Using computational fluid dynamics human nasal cavity models, however, we found essentially no difference in predicted olfactory odorant flux (g/cm2 s) for turbulent versus laminar flow for total nasal flow rates between 300 and 1000 ml/s and for odorants of quite different mucosal solubility. This lack of difference was shown to be due to the much higher resistance to lateral odorant mass transport in the mucosal nasal airway wall than in the air phase. The simulation also revealed that the increase in airflow rate during sniffing can increase odorant uptake flux to the nasal/olfactory mucosa but lower the cumulative total uptake in the olfactory region when the inspired air/odorant volume was held fixed, which is consistent with the observation that sniff duration may be more important than sniff strength for optimizing olfactory detection. In contrast, in rats, sniffing involves high-frequency bouts of both inhalation and exhalation with laminar airflow. In rat nose odorant uptake simulations, it was observed that odorant deposition was highly dependent on solubility and correlated with the locations of different types of receptors.

Modelling nasal airflow using a Fourier descriptor representation of geometry

“…While laminar restful breathing has been used here and verified for this model by in vitro experiment using an accurate replica [51], a greater spectrum of flow boundary conditions remains necessary in studying the link between form and function of the nasal cavity. This however remains beyond the scope of this work; current numerical modelling for turbulent flows [52,53] does not attempt to represent the laminar-transitional phenomena known to occur and observed in recent time-resolved experiments [41] Since results of the CFD analysis have shown that the reduced geometry formed using the dominant Fourier descriptors performs very similarly to the 'true' one, a future study of the dominant Fourier descriptors from a larger population may feasibly aid in classifying characteristic flow patterns associated with morphological modes. This could be developed further as an essential component in patient specific prognosis for clinical application.…”

How much does nasal cavity morphology matter? Patterns and rates of olfactory airflow in phyllostomid bats

“…Within the ethmoturbinate region is the olfactory recess, which is a blind pocket at the back of the nasal cavity. The principal function of the olfactory recess may be isolating the inhaled, odorant-laden air from exhaled respiratory air currents, which would otherwise be washed out by freshly inhaled odorants from the ethmoturbinate region [4][5][6][7].…”

“…dogs [3,5]; rats [6][7][8]), and those with a small olfactory region and poorly developed sense of smell on the other (e.g. humans [9,10]).…”

Моделирование Конвективных Потоков В Каналах Нерегулярной Формы На Примере Полости Носа И Околоносовых Пазух Человека

“…В зависимости от изменения или неизменности моделируемых параметров потока от времени следует выделить две большие группы, представляющие стаци-онарное (независимое от времени) и нестационарное (учитывающее изменение параметров потока во време-ни) моделирование. В качестве граничных условий в стационарных моделях задают массовый расход, напри-мер [2], или перепад давления [3]. В нестационарных моделях граничные условия также заданы как стацио-нарные при условии постоянного расхода на входе в носовую полость [4].…”

“…За по-следние десять лет в мире наблюдается всплеск по количеству публикуемых в данном направлении работ. Некоторые из них представлены нами в списке литерату-ры [2][3][4]. В зависимости от изменения или неизменности моделируемых параметров потока от времени следует выделить две большие группы, представляющие стаци-онарное (независимое от времени) и нестационарное (учитывающее изменение параметров потока во време-ни) моделирование.…”