Modeling Deposition of Inhaled Particles - Aerosols: Measurement, Dosimetry, and Health Effects - page 91

years in a wide variety of scientiic ields, especially in mechanical and aerospace engineering.
However, the scientiic validity of any CFPD simulation is based on the appropriate application of
these methods to the problem at hand. For example, the geometry of realistic respiratory system
passages is more irregular than that in many industrial or engineering applications. Appropriate
discretization of the complex geometry (for solution of the Navier-Stokes equations) may be very
dificult, and an invalid mesh may result in specious computational results. However, more robust
discretization (meshing) algorithms are continuously being developed and tested.
5.3.2.2 Biological Realism
Particle deposition models vary greatly in their level of biological realism. For example, empirical
models contain no information about particle motion or the physiology or anatomy of the respiratory
system (i.e., the respiratory system is treated as a “black box”), yet they may be useful in interpreting
data from experiments in which subjects breath well-characterized aerosols in a known manner.
Many deterministic models take into account both respiratory system morphology and the motion
of individual particles. They are, therefore, able to model deposition in different respiratory system
regions (extrathoracic, TB, or pulmonary), in individual lung airway generations or within single
airways.
Stochastic models may present a limited “anatomically realistic” model per se (i.e., surface
features not considered); however, they have the important advantage of being able to model the
realistic biological variability that is present among each lung pathway in a single subject and
between individual subjects. 79 Stochastic models by their construction provide estimations of intra-
and intersubject variability in deposition. 79
CFPD models are capable of simulating deposition in realistic airway conigurations, consider-
ing low conditions that arise from airway surface features. Therefore, CFPD models can predict
local deposition (i.e., at cells, bifurcations, carinal rings) caused by secondary low currents, which
cannot be predicted from simpliied analytical models. CFPD can be used to predict deposition in
complex anatomical geometries where the assumption of a smooth-walled cylinder should not be
made, for example, in the larynx, mouth, or nasal passages. A particular advantage of CFPD models
is that they provide the potential for coupling imaging studies with deposition modeling. Irregular
respiratory system morphologies can be extracted from CT or MRI images, and these morphologies
can provide a basis for CFPD studies.
5.3.2.3 Hardware and Software Issues
Deposition models of different classes can require vastly different hardware and software resources.
For example, empirical modeling requires no specialized computer programs, as the models are
simple algebraic relationships. Deterministic models (those derived from particle motion and low
equations using various simplifying assumptions) vary greatly in computational eficiency, based
on the nature of the assumptions made and the complexity of the airway geometry considered.
However, CFPD modeling is typically computationally intensive, requiring high-performance hard-
ware with adequate processing and memory resources. In general, CFPD also requires either expen-
sive third-party software or a large amount of complicated, challenging, in-house programming.
As an example, Zhang and Kleinstreuer 80 used the CFD package CFX 4.3 (AEA Technology, Inc.,
Carlsbad, CA) to perform their studies of secondary low patterns in an airway branching network.
The software ran on a multiprocessor Silicon Graphics workstation, and a typical run time for a
luid low and particle transport simulation for a single breathing cycle was approximately 72 h.
However, the use of CFPD models is becoming more frequent as the computing power of desktop
personal computers and workstations rapidly increases.
5.3.2.4 Advantages of Modeling and Simulation
So far we have focused on the merits and limitations of particular types of deposition models;
we would also like to comment on the advantages and challenges of using models in the study of