Thomas Wey and Nan-Suey Liu

Glenn Research Center, Cleveland, Ohio

Assessment of Microphysical Models in

the National Combustion Code (NCC) for

Aircraft Particulate Emissions: Particle Loss

in Sampling Lines

NASA/TM—2008-215304

October 2008

AIAA–2009–0257
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Thomas Wey and Nan-Suey Liu

Glenn Research Center, Cleveland, Ohio

Assessment of Microphysical Models in

the National Combustion Code (NCC) for

Aircraft Particulate Emissions: Particle Loss

in Sampling Lines

NASA/TM—2008-215304

October 2008

AIAA–2009–0257

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Prepared for the

47th Aerospace Sciences Meeting

sponsored by the American Institute of Aeronautics and Astronautics

Orlando, Florida, January 5–8, 2009
Acknowledgments

This work is supported by the NASA Fundamental Aeronautics Program. We would like to thank Drs. Chowen Wey and

Dan Bulzan for useful discussions on the selection of the validation data. The data of the APEX3 sampling lines are

provided by Dr. Bruce Anderson, and the data of UTRC sampling lines are provided by Dr. David Liscinsky.

Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5285 Port Royal Road

Springfi eld, VA 22161

Available electronically at http://gltrs.grc.nasa.gov

This work was sponsored by the Fundamental Aeronautics Program 

at the NASA Glenn Research Center.

Level of Review: This material has been technically reviewed by technical management. 

This report is a formal draft or working 

paper, intended to solicit comments and 

ideas from a technical peer group.

This report is a preprint of a paper intended for presentation at a conference. 

Because changes may be made before formal publication, this preprint is made available 

with the understanding that it will not be cited or reproduced without 

the permission of the author.
NASA/TM—2008-215304 1

Assessment of Microphysical Models in the National Combustion 

Code (NCC) for Aircraft Particulate Emissions: 

Particle Loss in Sampling Lines 

Thomas Wey and Nan-Suey Liu 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Summary 

This paper at first describes the fluid network approach recently implemented into the National 

Combustion Code (NCC) for the simulation of transport of aerosols (volatile particles and soot) in the 

particulate sampling systems. This network-based approach complements the other two approaches 

already in the NCC, namely, the lower-order temporal approach and the CFD-based approach. The 

accuracy and the computational costs of these three approaches are then investigated in terms of their 

application to the prediction of particle losses through sample transmission and distribution lines. Their 

predictive capabilities are assessed by comparing the computed results with the experimental data. The 

present work will help establish standard methodologies for measuring the size and concentration of 

particles in high-temperature, high-velocity jet engine exhaust. Furthermore, the present work also 

represents the first step of a long term effort of validating physics-based tools for the prediction of aircraft 

particulate emissions. 

Introduction 

Aircraft emissions contribute to climate change in the atmosphere and air pollution in the 

neighborhood of airports. Assessment of the magnitude of their environmental impacts calls for a better 

understanding of the formation and the subsequent development of gaseous pollutants, aerosols (volatile 

particles and soot), and their precursors in the internal and in the plume of the jet engines operating over 

the full range from ground to flight altitude. The volatile nature of aerosols introduces difficulties in their 

repeatable and accurate measurement because their concentration and size can be quite sensitive to the 

sampling conditions and procedures. These difficulties are sometimes further compounded by the 

necessity of in-situ measurements, i.e., sampling from aircraft in flight. Clearly, the ability to predict the 

effects of sampling conditions and procedures on the aerosol behavior will greatly assist the development 

of sampling techniques and the optimization of sampling procedures for making quantitatively accurate 

measurements of particulate emissions. 

A multi-dimensional CFD-based capability for modeling and simulation of the aviation-sourced 

particle evolution in flow paths is now available in the National Combustion Code (NCC). A detailed 

description of this multi-dimensional approach can be found in references 1 and 2. The CFD-based 

simulation is very computing intensive, and, quite often, a lower-order temporal approach is used to 

capture the overall evolution, a detailed description of this approach is given in reference 3. This lowerorder temporal approach greatly alleviates the computing resource requirement, but it may not be 

appropriate for certain flow configurations and conditions. To bridge the gap between the CFD-based 

approach and the lower-order temporal approach, we have implemented the fluid network approach into 

the NCC, the details of the adopted fluid network approach will be presented in the next section. 

The NASA-sponsored Aircraft Particle Emission Experiment (APEX) field test series to measure the 

size and concentration of particles in the jet engine exhausts have produced highly variable results (ref. 4). 

To better understand the sources of this variation, NASA recently sponsored additional field experiments 

and laboratory experiments to evaluate jet engine exhaust sampling and measurement systems. One of the 
NASA/TM—2008-215304 2

focused area is the assessment of size and mass-dependent particle loss through the sampling lines. These 

tests produced several sets of particle line loss data, which are used in the present effort to validate the 

predicted results obtained from the three different modeling and simulation approaches mentioned above. 

Comparison between the measured data and computed results are discussed in a later section. 

Modeling and Simulation 

Same Chemical and microphysical models for aircraft particulates are used in the three different 

levels of approach. The finite rate kinetics models used are those for conditions intermediate between 

combustion and atmospheric chemistry. The particle transport equations are based on the multi-bin 

Eulerian framework. The microphysical models include sulfate and nitrate aerosol precursors, 

thermophoresis, coagulation, nucleation, soot activation, condensation/evaporation, and interaction 

between the gaseous species and the particles. Generally speaking, the CFD-based simulation tightly 

integrates fluid dynamics, chemistry, and particle microphysics. The lower-order temporal simulation is 

one in which chemistry and particle microphysics are driven by averaged flow parameters specified as a 

function of time. Details of these two approaches have been reported in references 1, 2, and 3. 

Motivated by the practical need to simulate the often complicated sampling line system in an efficient 

and timely manner, we added an additional option based on the fluid network approach, which takes into 

account the complex internal flow and thermal effects within the fluid systems. Although here the 

intended application is for the particle sampling line system, the fluid network approach is, in general, a 

system level method and is not restricted to any specific geometries or configurations. SINDA/FLUINT 

(ref. 5) and GFSSP (ref. 6) are two of many system simulation programs widely used by NASA for 

internal flows. This work adopts similar formulation of the momentum equation as that in GFSSP. A 

relatively detailed description of the fluid network approach used in this work follows. 

The conservation equations for the gaseous flow and the transport equations for the aerosols are 

Continuity equation 

0

V A

d dV u dA

dt

+ ⋅ = ∫∫∫ ∫∫ G G ρ ρ (1) 

Species transport equation 

( ) m m m eff m VA A V

d dV u dA D dA w dV

dt

+ ⋅= ∇ ⋅+ ′′′ ∫∫∫ ∫∫ ∫∫ ∫∫∫ G G G  ρ ρρ ρ

ρ

(2) 

Momentum equation 

V A AA

d u dV uu dA pdA dA dt

+ ⋅ =− + ⋅ ∫∫∫ ∫∫ ∫∫ ∫∫ G G G G GG ρρ τ (3)

Energy equation 

V A A AA

d E dV Eu dA pu dA q dA u dA dt

+ ⋅ =− ⋅ − ⋅ + ⋅ ⋅ ∫∫∫ ∫∫ ∫∫ ∫∫ ∫∫ G GG G G GGG ρρ τ (4)

Aerosol general dynamic equation 

2 ( ) 3

t

kk M k VA A u u

d N dV N u dA D N dA

dt C f

+ ⋅= + ∇⋅ ∫∫∫ ∫∫ ∫∫ G G G ν
NASA/TM—2008-215304 3

k

V micro

dN dV

dt

+ , ∫∫∫

1 B k M = , , " (5) 

where 

q T = −∇ , G κ

2 1 ( ) ( ( )) 3 2 =− ∇⋅ + ∇ + ∇ , u

T I uu G G G τμ μ

V is an arbitrary control volume with control surface A, ρ is the fluid density, ρm is the partial density of 

species m, wm

′′′  is the production rate of species m per unit volume, u

G is the flow velocity in a stationary 

Cartesian coordinate system, E is the total energy density, p is the static pressure, τ is the viscous stress 

tensor, q

G is the heat flux vector, Deff is the effective mass diffusivity coefficient which is equal to t

Sc

μ +μ

ρ , 

κ, μ, and μt are the thermal conductivity, laminar viscosity and turbulent viscosity, respectively. For gas, 

the Schmidt number (Sc) is about 1. Nk is the number density of particles in size bin k, MB is the total 

number of the size bins, vt is turbulent kinematic viscosity, Cu is 0.09, fu is the damping function of the 

turbulence model. Detailed definitions of variables can be found in reference 1. 

In the framework of the fluid network approach, the entire fluid system is considered as consisting of 

a number of lumps connected by paths. Each lump has its own finite volume and is joined by path(s) to 

other lump(s), thus a network is formed. The fluid properties, such as density, temperature, pressure, 

viscosity, and mass fractions are computed and stored at the centers of lumps, while a unidirectional 

velocity is computed and stored in each path; its value can be less than zero, equal to zero or larger than 

zero. In general, the number of paths connecting to a lump can vary from lump to lump. In this work, the 

paths enter a lump are called inward paths, while the paths leave a lump are called outward paths. This 

definition facilitates the implementation of upwind differencing scheme and implicit time integration. 

In the following, the subscript for a path variable will be either j or A, and the subscript for a lump 

property will be i. This is the staggered-grid arrangement commonly used in many CFD schemes. In 

addition, a predictor-corrector algorithm is adopted to advance the temporal evolution of the variables. 

In the predictor step, a previously computed pressure is employed to solve the momentum equation 

for the path velocity, 

max( 0)( )

max( 0)( ) ( )

.

. ..

n jj jj j j juw

j jj j jdw uwj dwj j fj j uj

mu mu

m uu

t

m u u p p A Km m A S

∗

∗ ∗

∗ ∗

− + ,− + Δ

−, − = − − | | +

 (6) 

where the subscript juw points to the upwind path of path j, while the subscript jdw points to the 

downwind path of path j. Conversely the subscript uwj points to the upwind lump of path j, while the 

subscript dwj points to the downwind lump of path j. Here, uj is the velocity of path j, 

.

m j is the mass 

flow rate of path j, Kfj accounts for the laminar and turbulent viscous effects associated with the path j, it 

is a function of the geometry entities and the characteristics of the paths. For typical pipes, it is usually a 

function of Darcy friction factor. A comprehensive definition of Kfj is given in reference 6. Aj is the cross 

sectional area of path j. Suj represents a generic source term such as a pump or additional forces exerting 

on a path. 
NASA/TM—2008-215304 4

Equation (6) can be recast into 

2 max( 0) max( 0) ... j jjj fjj j j

m

K mA m m u

t

∗ ⎡ ⎤ + | | + ,+ − , ⎢ ⎥ Δ⎣ ⎦

ρ

max( 0) max( 0) ( ) . . n j j j j juw jdw uwj dwj j uj

m u

mu mu p pA S

t

∗ ∗ = , + −, + − + + Δ (7) 

In the subsequent discussion, we will use the variable .Mtj which is defined as 

2 max( 0) max( 0)

. . ... j tj jjj fj j j

m M K mA m m

t

= + | | + ,+ − , Δ ρ

The energy equation is formulated in terms of the conservation of the enthalpy. 

1 1

max( 0)( ) max( 0)( ) . . n outward inward

ii ii j i dw i uwj

j

mh mh m hh m hh

t

∗ ∗∗ ∗∗

= =

− + −, − − , − Δ ∑ ∑ A A

A

2

1

max( 0) ( )[( ) )] ( ) ( )

. . . outward

uw dw f dw i w wall aw

m A uA p p K m T T HA T T L = m

⎡ ⎤

− , ⎢ ⎥ = − + + −+ − ⎢ ⎥ ⎢⎣ ⎥⎦

∑ A A

AA A A A A A A A A A

A A A

κ

2

1

max( 0) ( )[( ) )] ( ) ( )

. . . inward j j j j j dwj uwj fj j uwj i j wj wall aw j

j j j

m A

uA p p K m T T HA T T L = m

⎡ ⎤ , ⎢ ⎥ + − + + −+ − ⎢ ⎥ ⎢⎣ ⎥⎦

∑ κ

hi +S (8) 

where the convective heat transfer coefficient is | | H Cp u St = ρ , and 2/3 Pr

2

f St − = is the Stanton 

number given by modified Reynolds analogy for turbulent flow in pipes, where f is the Fanning friction 

factor of the path, and Pr is the Prandtl number. Twall is the specified wall temperature and Taw is the 

adiabatic wall temperature. L is the normal distance of a path. Awj is the wetted surface area of path j. Shj

represents a generic source term for the energy equation such as the heat transfer from the surrounding 

solid. 

In the second, i.e., the corrector step, the continuity equation is formulated in terms of a pressure 

correction. It is assumed that the velocity is a function of the pressure gradient which, in turn, is a 

function of the two pressures on each side of a velocity path. Now consider a path j, which is connected to 

a upwind lump denoted by upj and a downwind lump denoted by dwj, applying a Taylor series expansion 

along the pressure coordinates, one has 

( )( ) j j

j j uwj uwj dwj dwj

uwj dwj

u u

uu p p p p

p p

∗∗ ∗ ∂ ∂

=+ − + −

∂ ∂

Using the definition of .M tj and equation (7), one finds 
NASA/TM—2008-215304 5

. . j jj j

uwj dwj tj tj

uA u A

p p M M

∂ ∂

= , =− ∂ ∂

thus, the new velocity can be expressed as a function of the previously predicted velocity and the pressure 

change via 

( ) .

j

j j uwj dwj

tj

A

uu p p

M

∗ = + Δ −Δ (9) 

Now applying the continuity equation to a lump yields 

1 1

0

n outward inward

i i

jj j

j

m m uA uA

t = =

− + −=

Δ ∑ ∑ AA A

A

ρ ρ (10)

Like other pressure-based methodology, the pressure change and the density change are related through a 

derivative evaluated at the condition of constant temperature: 

T

d

p dp

∗ ∗ ⎡ ⎤ = +Δ = +Δ ⎢ ⎥ ⎣ ⎦

ρ ρρ ρρ (11)

where, for the perfect gas, d 1

dp RT T

⎡ ⎤ = ⎢ ⎥ ⎣ ⎦

ρ , and, for the high pressure fluid model, 2d

dp T a

⎡ ⎤ = ⎢ ⎥ ⎣ ⎦

ρ γ . 

Substituting equations (9) and (11) into equation (10), and then neglecting the second order changes 

while applying the first-order-upwind differencing to the mass flux in the paths, one obtains the final form 

of the pressure correction equation 

2

1

max( 0) ( )

outward

i i

i dw

i i t

V p Au A

p p RT t R T M

∗ ∗

=

Δ ⎡ ⎤

+ − , + Δ −Δ ⎢ ⎥

⎣ ⎦ ∑ AA A A

A

A A A A + 

ρ

2

1

max( 0) ( )

inward jj j j

i uwj

j j tj j

Au A

p p R T M

∗ ∗

=

⎡ ⎤

+ , + Δ −Δ ⎢ ⎥

⎣ ⎦

∑ 

ρ

1

max( 0) max( 0)

outward

Au Au dw i

⎡ ⎤ ∗ ∗ ∗∗ ⎢ ⎥ ⎣ ⎦ =

= −, − , ∑ AA A AA

A

ρ ρ

1

max( 0) max( 0)

inward

j j uwj j j i

j

Au Au ⎡ ⎤ ∗ ∗ ∗∗

⎢ ⎥ ⎣ ⎦ =

+ , − −, ∑ ρ ρ

n m m i i

t

∗ − − Δ (12) 

where Vi is the volume of lump i. Once the pressure is obtained, the field values of the path velocity are 

corrected according to 
NASA/TM—2008-215304 6

1 ( )

. n uwj dwj

j jj

tj

p p u uA

M

+ ∗ Δ − Δ

= + (13) 

and the enthalpy values are corrected according to 

1 1 ( . n i i

i i

ti

p V h h

t M

+ ∗ Δ

= +

Δ

1

1

0 5( )

outward n p p Au i dw

⎡ + ⎤ ⎢ ⎥ ⎣ ⎦ =

+ . Δ +Δ ∑ A AA

A

1

1

0 5( ) )

inward n i uwj j j

j

p p Au ⎡ + ⎤ ⎢ ⎥ ⎣ ⎦ =− . Δ +Δ ∑ (14) 

where .

M ti is the average of surrounding .M t

 at lump i. 

For each species s, there is one transport equation to be solved respectively 

1

1 1

1

max( 0)( ) . n outward i si i si n n

s i s dw

my my

my y

t

+

, , + +

, ,

=

− + −, − Δ ∑ A A

A

1 1

1

max( 0)( )

inward . n n j s i s uwj

j

my y

+ +

, ,

=

− ,− ∑

1

( ) ( )

outward

t s dw s i

A

y y Sc L , ,

=

⎡ + ⎤ = − ⎢ ⎥ ⎣ ⎦ ∑ A A

A

A A

μ μ

1

( )

( )

inward t j j

s uwj s i

j j

A

y y Sc L , ,

=

⎡ + ⎤

+ − ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ∑ μ μ

. . i i s i s i

V V

, , + +

ω Ω (15) 
NASA/TM—2008-215304 7

The fluid network based GDE is written as 

1

1 1

1

max( 0)( )

n outward i ki i ki n n

k i k dw

VN VN

Au N N

t

+

, , + +

, ,

=

− + −, − Δ ∑ AA A

A

1 1

1

max( 0)( )

inward n n j j k i k uwj

j

Au N N + +

, ,

=

− ,− ∑

1

outward 4 d

i k dw

h

V

V N

D

⎡ ⎤ , ⎢ ⎥ , ⎢ ⎥ = ⎣ ⎦

= − ∑ A

A

A A

1

inward 4 d j

i k uwj

hj j

V

V N

D

⎡ ⎤ , ⎢ ⎥ , ⎢ ⎥ = ⎣ ⎦

− ∑

k i

i

micro

N

V

t

Δ , +

Δ

1 B k M = , , " (16) 

where Nk is the number density of particles in size bin k (the unit is #/m3), k loops from 1 to the maximum 

number (MB) of sections for each particle size distribution, Vd is the deposition velocity in a path, and Dh

the hydraulic diameter of a path. 

Nk

t micro

Δ

Δ is the summation of microphysical processes which contribute to the production and loss of the 

particles, 

k ki ki

micro thermolphoresis coagulation

N N N

tt t

Δ Δ Δ , , = +

ΔΔ Δ 

ki ki

nucleation cond evap

N N

t t

, ,

./ .

Δ Δ

+ +

Δ Δ (17) 

Finally, the solution of all the transport equations leads to 

[ ][ ] [ ] A B φ = (18) 

where [A] is a diagonal-dominated matrix, [B] is the source vector, and φ is the solution vector. 

Equation (18) is solved here iteratively using a point-implicit relaxation method due to the unstructured 

nature of the fluid network. 

Experiment 

Recently, laboratory tests have been conducted to better understand the sources of the significant 

variations in the measured size and concentration of particles in the jet engine exhaust among several 

sampling and measurement approaches. The APEX-3 sampling line efficiency results are presented in 

reference 7. Line loss measurements for different tubing sizes and sample flow rates are reported in 

reference 8. 

In reference 7, the particle loss characteristics of the sampling lines used in the APEX-3 field 

measurements were examined in a laboratory environment. The goal was to quantify the particle 
NASA/TM—2008-215304 8

penetration efficiency as a function of the particle diameter. In these tests, monodisperse particles were 

generated by a tube furnace, and two well characterized particle instruments were used, one was 

connected to the entrance of the tested sampling line, the other to the exit of the tested line. The particle 

penetration efficiency of the sampling line is defined as the ratio of the measured downstream particle 

number density to the measured upstream particle number density. The layout of the test sampling lines 

has the same geometries as used in the field measurements. Two sets of data were obtained in the 

laboratory tests, one is for the case of the so called 1m-probe line, i.e., the aerosol probe is located at 

1 meter downstream from the exit plane of the aircraft engine, the other one is for the case of the 30-m 

probe line. Schematics of the test setup can be found in the Appendix. 

In reference 8, the monodisperse particles were generated from a premixed flat flame burner. The 

particle penetration efficiency as a function of the particle diameter has been investigated for different 

material, length, diameter, and flow rate of the sampling line. A comprehensive line loss data sets are 

being compiled, and we have selected the test number 26 and test number 34 for modeling and 

simulation. The sampling lines are made of stainless steel, but they have different sizes, lengths, and flow 

rates. 

Assessment Results 

Four line loss experimental data sets are used for assessing the microphysical models currently in the 

NCC. These data sets are denoted as the APEX3 1-m probe sampling line (ref. 7), the APEX3 30-m probe 

sampling line (ref. 7), the UTRC test 26 sampling line (ref. 8), and the UTRC test 34 sampling line 

(ref. 8). Monodisperse particles have been used in all of these tests. And, for the flow conditions and the 

particle size range tested, the experimental data strongly suggest that diffusion of particles to the walls is 

the primary particle loss mechanism, other effects, such as the bends in sampling line, are negligible. 

Three different levels of simulation have been carried out: CFD-based simulation, network-based 

simulation, and lower-order temporal simulation. It should be noted here that all of them use the same 

chemical and microphysical models. Each calculation requires the specification of particle size 

distribution and concentration at the entrance of the sampling line, and, instead of monodisperse particles, 

we have used polydisperse particles for all of the numerical simulations. The 12-bin discrete distribution 

of these polydisperse particles is given in table 1. Calculations in which all of the chemistry and 

microphysical models for the particles were “turned on” have been performed, and the results are very 

much the same as those from the calculations which include only the effects of particle diffusion. This is 

consistent with the experimental suggestion that diffusion of particles to the walls is the primary loss 

mechanism in the cases considered, and other effects, such as coagulation among particles, are 

insignificant. It is mentioned here that the results presented in the following are obtained by using 

polydisperse particles with the option of coagulation being “turned off” in the calculations. 

TABLE 1.—DISTRIBUTION OF PARTICLE SIZE AND NUMBER DENSITY 

Bin number Diameter, 

(nm) 

Number density, 

(#/m3

) 

1 3 8585090 

2 4.48 453587191 

3 6.7 10742262056 

4 10 95160588390 

5 15 315315201902 

6 22.4 390804103104 

7 33.5 181175611146 

8 50 31417148866 

9 74.8 2037793720 

10 112 49440184 

11 167 448669 

12 250 1220 
NASA/TM—2008-215304 9

1-m particle probe sampling line

Since it is known that the effect of bends in the line on the particle loss is insignificant in this case, we 

consider a straight pipe consisting of two segments with different diameters. The total length of the 

sampling line is 23.5 m. The internal diameter of the first 9.5 m segment is 0.009525 m (3/8 in.). The 

internal diameter of the second 14 m segment is 0.019050m (3/4 in.). The CFD-based simulation assumes 

the flow is axisymmetric, and a total of 57,680 quadrilaterals are used to discretize the computational 

domain. Figure 1 shows a part of the mesh. The grid distribution used to conduct the lower-order 

temporal simulation is shown in figure 2, the number of grid points is 40. Similar arrangement is used in 

the fluid network simulation, i.e., there are 40 lumps and 39 paths. 

The incoming flow rate is 50 liter per minute; hence, the equivalent inlet velocity is 11.7 m/s. The 

temperature of the carrier gas is 295 K. The upstream pressure is 740 Torr (98627.2 Pa). The downstream 

pressure is 690 Torr (91963.2 Pa). Computed results and the experimental data in terms of the particle 

penetration efficiency are presented in figure 3. The parameter “a” used in the CFD-based simulation 

accounts for the sticking effect of the wall. The value of a = 0.5 means that the number density of the 

particles at the wall is half of the value at the nearest cell. Figure 3 indicates that, for smaller particles 

(say, d<10-20 nm), temporal and fluid network methods over-predict the penetration efficiency, while 

CFD-based method under-predicts the penetration efficiency. For larger particles, all three methods 

provide reasonably good results. 

Figure 1.—Schematic of the mesh used in the CFD-based simulation. 

Figure 2.—Schematic of the grid distribution used in the lower-order temporal simulation 

Figure 3.—Line loss comparison: flow rate = 50 LPM, inside diameter = 0.009525 m for 

the first 9.5 m segment, inside diameter = 0.01905 m for the second 14 m segment, 

Tgas=295K, Pout = 91963 Pa. 
NASA/TM—2008-215304 10

30-m particle probe sampling line

The simulated sampling line is a straight pipe with an internal diameter of 0.01905 m (3/4 in.). The 

total length of the sampling line is 35 m. The incoming flow rate is 50 liter per minute. Thus, the 

equivalent inlet velocity is 2.9 m/s. The temperature of the carrier gas is 295 K. The upstream pressure is 

740 Torr (98627.2 Pa), and the downstream pressure is 720 Torr (95961.6 Pa). Computed penetration 

efficiency of the particles and its experimental counterpart are presented in figure 4. For smaller particles 

(say, d<30-40 nm), all methods, except for the fluid network method with modified slip factor, overpredict the penetration efficiency. For larger particles, all methods yield reasonably good results. 

The number of grid points used in the lower-order temporal simulation is 50. The fluid network 

simulation uses 50 lumps and 49 paths. Two different meshes are used for the CFD-based simulation: 

75,040 quadrilaterals used in an axisymmetric simulation and 380,160 hexahedrons used in a threedimensional simulation. 

In the CFD-based simulations, in stead of using the gradient of particle number density to compute 

the molecular diffusion near the wall, we now take the approach of using the deposition velocity to 

compute the diffusion flux near the wall. Consequently, the value of the parameter “a” is now set to zero. 

Figure 4 also demonstrates the effect of the so called slip factor on the results obtained from the fluid 

network simulation. Typically, the Cunningham-slip factor is defined as 

1 1 1 (1 257 0 4 ) K i n Cc Kn e i i − . / =+ . +. (19) 

where Kni is the Knudsen number defined as the ratio of the mean free path of the carrier gas, λg, to the 

diameter of the size-i particle 

Figure 4.—Line loss comparison: flow rate = 50 LPM, inside diameter = 0.019050m, 

Length = 35 m, Tgas = 299K, Pout = 95961 Pa. 
NASA/TM—2008-215304 11

2 g

i

p i

Kn

d ,

= λ(20)

The mean free path of the carrier gas depends upon the pressure, temperature, molecular weight of the 

carrier gas, and the universal gas constant. 

It is observed that, for smaller particles, equation (19) leads to values of Cci which are much larger 

than one, while for larger particles, the values are close to one. To allow more control of the values of this 

empirical parameter, equation (19) is modified as 

1 1 1 (1 257 0 4 ) b Kni Cc Kn e ii i − . / =+ . +. × (21) 

where bi is an particle-size dependent parameter. Experiences show that, generally speaking, when bi is 

larger than one, it leads to a reduction in the computed penetration efficiency. In the present simulation, 

values between 1.0 and 1.2 are specified for bi, depending on the particle size range. 

UTRC Test 26 sampling line

The tested sampling line is a straight pipe having an internal diameter of 0.0081026 m (0.319 in.). 

The total length of the sampling line is 15.24 m. The incoming flow rate is 50 liter per minute. Thus, the 

equivalent inlet velocity is 16.2 m/s. The temperature of the carrier gas is 298.6 K. The upstream pressure 

is 648.9 Torr (86512 Pa), and the downstream pressure is 540.2 Torr (72020 Pa). 

The CFD-based simulation assumes axisymmetric flow, and 34,720 quadrilaterals are used for the 

mesh. The number of grid points used in the lower-order temporal simulation is 31, the fluid network 

simulation uses 31 lumps and 30 paths. Computed as well as measured penetration efficiency are shown 

in figure 5. For smaller particles (say, d<10 nm), all numerical methods predict similar results. For larger 

particles, both CFD-based simulation and lower-order temporal simulation under-predict the penetration 

efficiency with respect to the experimental data. The results from the fluid network simulations compare 

well with the measured data. 

UTRC Test 34 sampling line

The tested sampling line is a straight pipe having an internal diameter of 0.011684 m (0.460 in.). The 

total length of the sampling line is 30.48 m. The incoming flow rate is 72.5 liter per minute; hence, the 

equivalent velocity is 11.3 m/s. The temperature of the carrier gas is 299.9 K. The upstream pressure is 

632.5 Torr (84324 Pa), and the downstream pressure is 551.6 Torr (73539 Pa). 

The CFD-based simulation is assumed to be axisymmetric, and the mesh has 75,040 quadrilaterals. 

The lower-order temporal simulation uses 46 grid points. The fluid network simulation uses 46 lumps and 

45 paths. Computed and measured penetration efficiencies are presented in figure 6. The predictions from 

the fluid network simulation with modified slip factor and the lower-order temporal simulation agree with 

the experimental data very well. The result from the fluid network simulation without modified slip factor 

over-predicts the penetration efficiency. 
NASA/TM—2008-215304 12

Figure 5.—Line loss comparison: flow rate = 50 LPM, inside diameter = 0.00810265 m, Length = 

15.24 m, Tgas = 298.6 K, Pout = 72042 Pa. 

Figure 6.—Line loss comparison: flow rate = 72.5 LPM, inside diameter = 0.011684 m, Length = 

30.48 m, Tgas = 299.9 K, Pout = 73539 Pa. 
NASA/TM—2008-215304 13

Concluding Remarks 

A fluid network simulation capability is now available in the NCC. Together with the previously 

available CFD-based simulation capability and the lower-order temporal simulation capability, we now 

have a suite of computational tools which are versatile and cost effective for the modeling and simulation 

of transport of aerosols in the various flow paths, such as the engine internal passages, the sampling 

probes, the sampling lines, and the plumes. The focus of the present effort is the assessment of these three 

levels of simulation when applied to the prediction of particle loss in the sampling lines used in the 

measurement of particulate matter size and concentration in high-temperature, high-velocity jet engine 

exhaust. The predicted particle penetration efficiencies have been compared with the corresponding 

experimental data from four different laboratory tests. Generally speaking, the predicted results agree well 

with the measured data, when the particle diameters are larger than, say, 30 nm. Based on the results 

obtained so far, among the three simulation approaches, the fluid network approach with modified slip 

factor seems to be the optimum choice for the modeling and simulation of the sampling line system. In 

addition to help establish standard methodologies for measuring the size and concentration of particles in 

the jet engine exhaust, the present work also represents the first step of a long term effort of validating 

physics-based tools for the prediction of aircraft particulate emissions. Obviously, this requires the 

acquirement of particulate emissions data specifically for the purpose of validating the microphysical 

models for aerosols. 

References 

1. Wey, T. and Liu, N.-S., “Modeling of Aerosols in Post-Combustor Flow Path and Sampling System,” 

NASA/TM—2006-214397, September 2006. 

2. Wey, T.C. and Liu, N.-S., “Modeling Jet Engine Aerosols in the Postcombustor Flow Path and 

Sampling System,” Journal of Propulsion and Power, vol. 23 no. 5 September-October 2007, pp. 

930–941. 

3. Wey, T.C. and Liu, N.-S., “Streamline-Based Modeling of Aerosol Microphysics in the PostCombustor Flow Path and Sampling System,” AIAA Paper 2007-0794, 45th AIAA Aerospace 

Sciences, Meeting and Exhibit, January 8–11, 2007, Reno, NV. 

4. Wey, C.C., Anderson, B.E., Wey, C., Miake-Lye, R.C., Whitefield, P., and Howard, R., “Overview 

on the Aircraft Particle Emissions Experiment,” Journal of Propulsion and Power, vol. 23 no. 5 

September-October 2007, pp. 898–905. 

5. C&R Technologies, “SINDA/FLUINT General Purpose Thermal/Fluid Network Analyzer Version 

5.0,” User’s manual, Oct. 2006, Littleton Colorado. 

6. Majumdar, A., “Generalized Fluid System Simulation Program (GFSSP) Version 3.0,” November 

1999, Sverdrup Technology, Inc. Huntsville, AL. 

7. Anderson, B.E., “Preliminary Results of the APEX-3 Sampling Line/Probe Efficiency Tests,” JETSAPEX2 and APEX3 Conference, November 29–December 1, 2006, North Olmsted, OH. 

8. Liscinsky, D.S., “Effect of Particle Sampling Technique and Transport on Particle Penetration at the 

High Temperature and Pressure Conditions found in Gas Turbine Combustors and Engines,” NASA 

Subsonic Fixed Wing NRA Annual Review, February 12, 2008. 

NASA/TM—2008-215304 15

Appendix—Laboratory Test of APEX-3 Sampling Lines 

To determine the particle loss characteristics of the sampling lines used in the APEX-3 field 

measurements, laboratory tests were conducted to quantify the particle penetration efficiency as a 

function of the particle diameter. Monodisperse particles were generated by a tube furnace. Two well 

characterized particle instruments are used, one is connected to the entrance of the tested sampling line, 

the other is connected to the exit of the tested line. The particle penetration efficiency of the sampling line 

is the ratio of the measured downstream particle number density to the measured upstream particle 

number density. 

The layout of the test sampling lines has the same geometries as used in the field measurements. Two 

sets of data were obtained in the laboratory tests, one is for the case of the 1m-probe line, i.e., the aerosol 

probe is located at 1 m downstream from the exit plane of the aircraft engine, the other one is for the case 

of the 30-m probe line. 

The following schematics are taken from reference 7 to illustrate the laboratory line test setup, the 

layout and geometry of the 1m-probe line, and the layout and geometry of the 30-m probe line: 

Laboratory test 

Line Loss Test Setup 

Distribution Manifold

Dilution Gas 

Particle instruments 

were tested side-byside prior to 

experiment to 

determine any 

differences in size 

sensitivity or 

response. 

3022 CPC 

Monodisperse Aerosol 

Source (NaCl from Tube 

Furnace) 

P 

3022 CPC 

P

System was carefully 

leak-checked to 

minimize stray particle 

counts to allow going 

to high sample 

dilutions/flow rates 

Pump 

MFC 

System flow was 

adjusted to simulate 

actual sampling 

conditions and to 

evaluate losses as a 

function of flow rate. 

MFM 

Sampling line 

to be tested 
NASA/TM—2008-215304 16

APEX-3 1-m probe line 

APEX-3, Aerosol Sampling Line

Mother Ship

Rack-mounted 

selector box

1 m, 3/8”

stainless

13 m, 3/4”

stainless

2 m, 3/8”

stainless

2” radius bend

3” radius bends

6” radius bend

6” radius bend

3/8” stainless 

flow splitter

1 m, 3/4”

stainless

Valve Box

Distribution 

Manifold

6 m, 3/8”

stainless

0.5 m, 3/8”

stainless

APEX-3 30-m probe line 

APEX-3, Aerosol Sampling Line

Rack-mounted 

selector box

0.3 m, 

3/4” 2” radius bend

2” radius bends

32 m, 3/4”

stainless

Distribution 

Manifold

1 m, 

3/4”

1 m, 3/4”

stainless
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Assessment of Microphysical Models in the National Combustion Code (NCC) for Aircraft 

Particulate Emissions: Particle Loss in Sampling Lines 

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14. ABSTRACT

This paper at first describes the fluid network approach recently implemented into the National Combustion Code (NCC) for the simulation 

of transport of aerosols (volatile particles and soot) in the particulate sampling systems. This network-based approach complements the other 

two approaches already in the NCC, namely, the lower-order temporal approach and the CFD-based approach. The accuracy and the 

computational costs of these three approaches are then investigated in terms of their application to the prediction of particle losses through 

sample transmission and distribution lines. Their predictive capabilities are assessed by comparing the computed results with the 

experimental data. The present work will help establish standard methodologies for measuring the size and concentration of particles in 

high-temperature, high-velocity jet engine exhaust. Furthermore, the present work also represents the first step of a long term effort of 

validating physics-based tools for the prediction of aircraft particulate emissions. 

15. SUBJECT TERMS

Particulate matter emissions 

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