Validation for CFD 

Prediction of Mass 

Transport in an Aircraft 

Passenger Cabin

A. J. Baker

S. C. Ericson

J.A. Orzechowski

K.L. Wong

University of Tennessee

Knoxville, TN 37996

R. P. Garner

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

November 2006

Final Report

DOT/FAA/AM-06/27

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine technical reports are 

available in full-text from the Civil Aerospace Medical Institute’s publications 

Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-06/27 

4. Title and Subtitle 5. Report Date 

Validation for CFD Prediction of Mass Transport in an Aircraft Passenger 

Cabin

November 2006 

6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Baker AJ,a Ericson SC,b Orzechowski JA,bWong KL,c Garner RP d 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) a

 U. Tennessee, Engineering Science Graduate program 

b

 U. Tennessee, Computational Fluid Dynamics Laboratory 

c

 U. Tennessee, Joint Institute for Computational Science 

d

 Federal Aviation Administration, Civil Aerospace Medical Institute 

(former employee) 

 

11. Contract or Grant No. 

 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 14. Sponsoring Agency Code 

800 Independence Ave., S.W. 

Washington, DC 20591 

15. Supplemental Notes 

Work was accomplished under approved cooperative research and development task. 

16. Abstract 

A joint project was established to validate computational fluid dynamics (CFD) as a quantitative methodology for 

prediction of the distribution of pathogens released into the environmental control system (ECS)-generated 

ventilation flowfield of an aircraft passenger cabin. Acquisition of the requisite experimental databases for threedimensional velocity and contaminant distributions was accomplished in the FAA Civil Aerospace Medical 

Institute’s (CAMI’s) Aircraft Environmental Research Facility (AERF). The associated CFD simulations were 

conducted by the University of Tennessee CFD Laboratory staff, on the resident Beowulf PC cluster and/or the 

University of Tennessee Innovative Computing Laboratory SiNRG cluster, using both commercial and 

proprietary CFD computer codes. The results of this CFD validation project are reported herein. 

 

 

17. Key Words 18. Distribution Statement 

Aircraft Airflow, CFD Analysis, Environmental Control 

System, Aircraft Cabin Airflow, Aircraft Ventilation 

Flowfield, Aircraft Contaminant Distribution 

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, VA 

22060; and the National Technical Information 

Service, Springfield, VA 22161

 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 55 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

iii

Executive Summary

A joint project is established to validate computational fluid dynamics (CFD) as a quantitative methodology for prediction of pathogen transport via the environmental control system (ECS)-generated ventilation 

flowfield of a commercial aircraft passenger cabin. Acquisition of the requisite experimental database for 

three-dimensional velocity and gaseous contaminant distributions is ongoing in the FAA Aircraft Environmental Research Facility (AERF) under the auspices of the Civil Aerospace Medical Institute (CAMI). The 

CFD simulations are executed on the University of Tennessee CFD Laboratory Beowulf PC cluster, and/or 

the University of Tennessee Innovative Computing Laboratory SiNRG cluster, using both commercial and 

proprietary CFD computer codes. 

The results of this CFD validation project, spanning two contract periods, are reported herein. The 

selected sonic anemometer experimental data protocol has proven most appropriate for measurement of 

the characteristically unsteady ventilation velocity field in an aircraft passenger cabin. Consequently, a firm 

knowledge base is established for characterizing factors affecting time-accurate, 3-dimensional CFD prediction of unsteady ventilation flowfields in the AERF, hence validation of CFD as an incisive technology for 

meeting technical goals. 

The CFD mass transport validation component proved more challenging to interpret, due principally to 

limitations inherent in the experimental protocol and measurement equipment. Notwithstanding, specifically designed and executed CFD computational experiments do provide clear insight into aircraft cabin 

ventilation design issues dominating time-dependent mass transport, hence CFD simulation fidelity.

The computer demand distinction between the commercial-steady RaNS CFD codes and time-accurate 

CFD algorithms/codes is clearly a dominating factor in application to contaminant transport simulation 

in aircraft cabins. Using the combination of these capabilities appears most important, with the principal 

value of the steady codes being initial-condition generation for the ensuing time-accurate simulations. 

The AERF proved to be an exceptionally valuable facility supporting acquisition of advanced knowledge 

affecting the fate of contaminants introduced into an aircraft passenger cabin. However, only the CFD component of this project progressed to evaluation in fully configured cabins, including seat rows and gaspers. 

Notwithstanding, these results, in total, provide the FAA with a firm understanding of key factors affecting 

aircraft cabin ventilation association with transportation safety issues. This knowledge base can contribute 

guidance on potential damage mitigation strategy assessments, should an event be detected.

v

Contents

INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

EXPERIMENTAL PROTOCOLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Transported mass distribution measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

COMPUTATIONAL PROTOCOLS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Geometry, mesh generation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Mass transport measurements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

DISCUSSION AND RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

CFD velocity field prediction specifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Baseline cabin CFD velocity field validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Cabin mesh refinement CFD study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Baseline cabin mass transport CFD validation exercise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Mass transport fidelity CFD experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

VHP temporal evolution CFD prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Fully configured cabin velocity field CFD experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Fully configured cabin mass transport CFD experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Recommendations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

1

Validation for CFD Prediction of Mass Transport

in an Aircraft Passenger Cabin

INTRODUCTION

Noxious material may be intentionally or unintentionally released into an aircraft cabin during flight. Reaction 

management for this potential disaster must be based on 

predictive quantitative methodologies applied to potential 

scenarios adversely affecting cabin environmental quality. The direct option would employ in-flight testing, 

but this approach is prohibitively expensive, logistically 

awkward, and generates strictly airframe platform- dependent results. 

The alternative is to employ computational fluid 

dynamics (CFD) to generate approximate solutions of 

the differential equation systems associated with the 

conservation principles. Closure models for turbulence 

are included that describe momentum and mass transport by and within velocity fields associated with aircraft 

ventilation. As with all engineering models, confidence 

in predictive capability rests completely on validation of 

the selected methodology. The CFD challenge, unique to 

aircraft cabin mass transport simulation, is the low speed, 

modestly turbulent unsteady character of the associated 

flowfields. Specifically, by design, aircraft environmental 

control system (ECS) operation at cruise generates barely 

perceptible velocity field variations associated with their 

low volumetric (air-changes per hour, ACH) operation. 

While CFD is a validated methodology for prediction of 

high-speed aerodynamic flowfields, the intricacies of its 

application to this very subtle-type flowfield is a current 

research, hence validation, topic.

Systems for ventilating inhabited spaces are designed 

to produce flowfields barely discernible by an immersed 

individual, in meeting the requirement for “comfort” (c.f, 

1). Nielsen presented the first CFD ventilation velocity 

field prediction in a 2-dimensional enclosure in 1974 [2]. 

Following the European lead [3,4], the 1990s produced 

a significant interest in ventilation velocity field CFD 

prediction, with American Society for Heating, Refrigerating, and Air-Conditioning Engineers Transactions being a 

publication vehicle of choice [5–9]. The inaugural CFD 

prediction of the 3-dimensional ventilation flowfield in 

an aircraft passenger cabin was published by Baker et al. 

[10], closely followed by Jones et al. [11]. The first CFD 

velocity field validation exercise for an aircraft cabin was 

reported at the 2003 Ventilation Conference [12]. 

To date, the authors are not aware of the publication 

of a validation exercise for mass transport in an aircraft 

passenger cabin environment. CFD exercises pertinent 

to mass transport prediction in inhabited spaces were 

reported by Baker et al. [13] and Memarzadeh [14]. CFD 

algorithm issues pertinent to mass transport prediction 

accuracy are summarized in Baker et al. [15].

Applications of CFD methodology to inhabited space 

flowfield prediction have employed computer codes of 

numerous origin, including commercial, national, and 

local research versions. The fundamental mathematical 

theory underlying these various CFD codes for predicting 

essentially incompressible flowfields is uniformly based 

on an iterative “pressure projection” concept. “SIMPLE” 

is the legacy name associated with this theory, and individual developments employ many variations thereon 

that distinguish specific codes. Williams and Baker [16] 

provided the definitive theory, enabling discerning analysis 

of the range of implementation options, which center 

on “computational” versus “genuine” pressure fields, 

hence boundary conditions for the associated Poisson 

equations.

An additional distinguishing character of pressure 

projection theory codes is the numerical diffusion scheme 

required to stabilize iterative convergence difficulties 

induced by the convective non-linearity contained in 

the parent Navier-Stokes (NS) momentum conservation 

principle. Commercial codes generally contain first-, 

second-, and third-order numerical diffusion options, and 

the instructions suggest that this sequence be utilized in 

generating a given solution. The University of Tennessee CFD Lab research and prototype production codes 

contain a calculus-derived, tensor product numerical 

diffusion operator, which is third-order with an adjustable coefficient [17], or fourth-order with a theory-fixed 

coefficient [18]. 

No “physics” assumptions are required to perform a 

laminar flow simulation. Time-averaging the NS equations 

leads to the Reynolds-averaged (RaNS) system, which 

exposes a (Reynolds) stress tensor for characterization of 

turbulent flow processes. The most common closure for 

RaNS conservation law systems is one of several two-equation “TKE” models, e.g., k-epsilon, k-omega [19, 20]. 

This adds a pair of non-linear, elliptic partial-differential 

equations requiring boundary condition specifications, 

especially on solid surfaces. The usual implementation 

avoids resolution of near-wall state variable variations 

via use of the “log law” similarity correlation [19]. An 

alternative closure model avoiding this issue is the Spalart-
2

Allmaras single-equation eddy viscosity transport model 

[21]. Commercial codes also purport to have a large eddy 

simulation (LES) option [22] that involves a spatial NS 

averaging operation that produces four stress tensors 

characterizing turbulence phenomena. The commercial 

codes used in this project simplify the theory to a single 

(Reynolds) stress tensor for closure modeling. An LES 

formulation requires execution using a time-accurate 

CFD algorithm.

For this project, CFD simulations have been conducted 

using two firmly established commercial codes [23, 24], 

and the University of Tennessee CFD Lab research and 

prototype-production CFD codes [25, 26]. The University 

of Tennessee CFD Lab codes are specifically designed to 

readily adapt to improved numerics and/or closure models 

as developed. The former very efficiently generate steady 

solutions for the RaNS system using either the TKE or 

Spalart-Allmaras closure models. For mass transport, or 

unsteady flowfield prediction, these codes also contain 

a second-order accurate time integration option that is 

much slower to execute. This option is required as well 

when executing, using the cited LES formulation with 

its closure model. The University of Tennessee CFD Lab 

codes operate only in time-accurate mode, for laminar 

flow, and for turbulent flow using a TKE closure model. 

All codes can execute in parallel.

EXPERIMENTAL PROTOCOLS

Velocity vector field measurements

The key issue is experimental acquisition of high quality, 3-dimensional velocity vector data to support CFD 

validation. For this project, experimental velocity field 

data were collected at the CAMI AERF, Figure 1, a retired 

Boeing 747-100. A review of appropriate experimental 

methodologies confirmed that a sonic anemometer best 

met requirements for time-accurate, 3-dimensional data 

collection. Such a system was acquired and installed in 

an isolated portion of the tourist passenger cabin with 

seat rows removed, Figure 2a). Each anemometer [27] 

simultaneously recorded the three cartesian components 

of the AERF ECS-generated ventilation velocity vector 

at data rates from 3 to 60 hz, plus the local temperature. 

Figure 2b) illustrates the designed data acquisition matrix 

in the cabin transverse plane, which was interrogated at 

the nominal seat row pitch along the entire axial span of 

the isolated cabin segment.

The sonic anemometer is particularly appropriate to 

velocity vector field measurement of ventilation type. The 

selected anemometer is sensitive to a velocity component 

magnitude of order 0.03 m/s in a nominally spherical 

sample volume with diameter the order of one centimeter 

[27]. The simultaneously acquired 3-dimensional data 

contains the true velocity vector time-history at each 

sample station. These data can then be time-averaged for 

determination of the RaNS mean flow component, which 

may itself be time-dependent, and the rms fluctuation 

distributed about the identified mean. This data operation can thus provide firm quantification of the level of 

“turbulence” in the ECS-distributed velocity field. 

The velocity field experiment acquisition protocol 

for the AERF was designed to minimize the effects of 

drift over the lengthy data collection period. Once the 

anemometers were in place, the AERF ventilation system 

was energized and allowed to come to equilibrium. When 

this was determined to have occurred (after about 20 min), 

the data stream from each anemometer was recorded for 

5 min. The experiment tree was then moved to a new 

location in the test matrix, with stabilization followed 

by the 5-min data-recording period. This operation was 

Figure 1. FAA Civil Aerospace Medical Institute Aircraft Environmental Research Facility, Mike Monroney Center, Oklahoma City, OK.
3

Figure 2. 3-D sonic anemometer installation in 

AERF. a) (left) velocity measurement sample volume with installation motion, b) velocity measurement grid map.

Figure 3. Graphic illustrating Boeing 747 ECS cabin ventilation supply plenum air introduction into passenger cabin. View facing cabin sidewall intersection with overhead 

luggage carrier.
4

Figure 4. Graphic illustrating introduction of VHP stream into AERF through 

nominal 4-in diameter duct. a) (above) global view facing cabin sidewall, b) 

(lower left) view upwards towards overhead luggage carrier, c) (lower right) view 

towards rear of cabin showing injection angle.
5

repeated at all locations on each cabin transverse half-plane 

over the length of the isolated cabin segment. 

Figure 3 illustrates the wall jet character of the AERF 

ventilation supply flow. The physical size of the sonic 

anemometer head precludes velocity measurement in 

the wall jet region. Hence, flow speed in this region was 

recorded using a conventional hot wire from the supply 

plenum outflow face along and into the outer reach of the 

wall jet attached to the luggage carrier underside. Since 

flow in this region is unidirectional, conversion of speed 

to velocity vector is readily accomplished.

As the final component of aircraft cabin ventilation 

flow experimental measurements, the CAMI AERF 

sonic anemometer test apparatus was flown in a Boeing 

747-400. The system measured the at-altitude cruise 

ECS-generated ventilation velocity vector field in locations above the elevation of the seat backs in the empty 

tourist section using the test protocol developed for the 

AERF. These data served as a validation base for the 

measurements obtained in the AERF using the identical 

experimental protocol. 

Transported mass distribution measurements

The goal was to track a time-dependent mass species 

distribution within the AERF ventilation system flow 

field. This was accomplished by introducing vaporized 

hydrogen peroxide (VHP) into a cabin segment just aft of 

the aircraft entrance door in front of the wing. The cabin 

segment is 32 ft long and isolated by two bulkheads built 

specifically for the purpose. Tape and plastic sheeting were 

used throughout the cabin to minimize leakage during 

the tests, the details of which are summarized in [28].

VHP was introduced into the cabin at the terminus 

of the piping system shown in Figure 4a). The outlet 

from the generator passed into the cabin segment via a 

nominal 3-in diameter pipe penetrating the aft bulkhead. 

The pipe diameter was sized to ensure the VHP inflow 

velocity would exert minimum impact on the ECSgenerated ventilation air introduction design. The aft 

bulkhead is the zero reference point of the z-axis of the 

Cartesian grid spanning the cabin segment. The VHP 

supply tube terminated in a 90-degree bend located z 

= 108 in from the aft bulkhead. The exit of the supply 

elbow, of nominal internal diameter 3.5 in, was centered 

73 in above the floor (y direction), Figure 4b), with the 

normal coordinate of the exit face surface aligned at a 

nominal 45° angle with the horizontal, Figure 4c).

A single VHP generator was located outside the aft 

bulkhead, Figure 5a). The generator is a completely selfcontained bio-decontamination system with the ability 

to dehumidify, generate vaporized hydrogen peroxide, 

and aerate sealed enclosures. The nominal generator 

operating condition was 35% solution of VHP in air at 

a rate of 12 g/min. 

The time evolution of mass concentration level was 

acquired using a near-infrared spectrophotometer sensitive 

to both VHP and water vapor [29]. Figure 5b) presents 

a composite view of the experimental layout (Note: The 

fan was not in operation during these experiments). The 

sensors were attached to a vertical wire with x-z coordinates lying in the y-z plane of the center of the VHP 

supply elbow, (Figure 5c). Each measurement at each 

sensor coupon location involved a 10-min test period. 

The spectrophotometer data rate was 10 s, recorded via 

a serial RS-232 signal sent to a PC running LabVIEW 

data acquisition software [30]. Figure 5d) shows a pair of 

guided wave monitors with probe cells mounted.

COMPUTATIONAL PROTOCOLS

Geometry, mesh generation

The CFD simulation 3-dimensional solution domains 

are the product of the symmetric half-span of the AERF 

cabin segment with various cabin-axis spans selected such 

that boundary condition imposition did not interfere 

with simulation fidelity. The ventilation supply plenum 

runs the length of the fuselage, sandwiched between 

the sidewall and the sidewall-adjacent overhead luggage 

carrier. The cabin exhaust plenum also runs the length 

of the fuselage at floor level adjacent to the sidewall. 

Defining measurements were taken of all cabin interior 

features, the supply slot jet geometry, and the essential 

geometry of both the supply and exhaust plenums to the 

extent accessible. 

The representative 3-D solution domain slice for CFD 

prediction of AERF velocity vector fields is graphed in 

perspective in Figure 6a). The supply plenum corresponds 

to the vertical appendage in the upper right side, while 

the exhaust plenum is the vertical appendage in the lower 

right corner. The rounded surface to the right is the fuselage sidewall, while the left vertical surface is the cabin 

symmetry plane. The contoured overhead luggage carriers 

appear as flow blockage insets in the solution domain. 

The external dimensions of the base CFD simulation 

domain block are approximately 100 in floor to ceiling, 

120 in from sidewall to center plane, and 31 in axial 

span, the approximate seat row pitch. This domain block 

was then duplicated and appended as necessary to admit 

farfield boundary condition specifications pertinent to 

a specific test.

Extensive computational experience has solidly confirmed that the CFD solution domain must include 

portions of both supply and exhaust plenums to avoid 

boundary condition (BC) specifications in velocity 
6

Figure 5. Steris VHP generation, distribution, and measurement system installation in 

AERF. a) (above left) VHP generator located exterior to cabin segment, b) (lower left) composite view of measurement system in cabin segment, c) (above right) hanger system for 

holding sample measurement coupons, d) (lower right) guided wave monitors with probe 

cells mounted.
7

Figure 6. Generated computational mesh of hexahedron-shaped cells for a segment of the AERF 

cabin without seat rows. a) (left) perspective view illustrating geometric essence, b) planar cross-section showing solution-adapted, non-uniform meshing for local field resolution.

Figure 7. VHP terminus region mixed tetrahedron-hexahedron mesh. a) (left) perspective view illustrating geometric essence, b) planar cross-section showing solution-adapted non-uniform meshing for 

local field resolution.
8

merging-entrainment regions wherein the flow state is 

not a priori known. The inflow BC applied at the supply plenum entrance plane is a slug supply velocity and 

an assumed turbulence level. The passage length of the 

supply plenum is sufficient to convert this unimaginative 

BC into a solution to the RaNS equations prior to the 

flow becoming the supply slot jet. The BC specification 

appropriate at the exhaust plenum is uniform pressure 

and vanishing normal derivative for the velocity and 

turbulence closure variables across the outflow plane. 

Following solution-adapted mesh alteration studies for 

wall jet and free jet resolution, the base hexahedral cell 

mesh discretization for the generic cabin slice containing both luggage carriers, rotated to a transverse plane 

view, is shown in Figure 6b). The resultant quite nonuniform mesh contained 14,300 nodes per mesh block 

plane. Baseline validation exercises employed one to nine 

co-joined blocks, uniformly spaced at 0.3 m intervals in 

the z-direction in the cabin region containing both luggage carriers. An 11-plane mesh was created to include 

the region where the central luggage carrier terminates. 

Therein, axial-direction non-uniform progression factors 

redefined z-plane spacing to provide axial mesh resolution in the central carrier terminus region, and domain 

meshing was added into the 3-D region that had been 

the interior of this luggage carrier.

The base cabin mesh block was then altered for inclusion of a mesh sub-block containing the VHP piping 

system and terminus, designed to induce minimal disturbance to the cabin ventilation supply slot jet flowfield 

(Figure 4). The injectant supply duct enters the CFD 

solution domain coincident with the small circle visible 

just below the supply plenum (Figure 6a). Figure 7a) 

presents a perspective view of the mesh block containing 

the injectant supply system terminus, which replaces the 

base mesh block. The flow in the 4-in diameter PVC pipe 

undergoes a terminal 90° turn, clearly visible therein, 

which introduces the injectant for entrainment into the 

wall-remote edge of the ventilation system wall jet attached to the overhead luggage carrier. 

The VHP injection system terminus generates the need 

for a mixed mode discretization containing both hexahedron and tetrahedron-shaped computational cells. The 

tetrahedron “unstructured” meshing more readily enables 

the local refinements required for adequate resolution of 

flows about the quite non-regular geometry. Figure 7b) 

presents the generated mesh in a plane, rotated to planar 

view, just upstream of the 90° bend of the supply duct. 

This piping-terminus system was designed to enable 

non-intrusive introduction of the VHP injectant. Data 

confirmed that the terminus face average velocity of the 

VHP was 0.7 m/s, which is quite modest compared to 

the supply slot jet average speed of 5 m/s. The injectant 

BCs were a slug velocity and assumed turbulence level 

at the entrance plane of the piping system, the circle 

in Figure 6a). The mass transport CFD simulation is 

required executed time accurate; therefore, the initial 

condition (IC) is mass fraction level at this plane and 

zero everywhere else.

CFD code selections

The CFD codes utilized in this study were two fully 

established commercial products [23, 24], the University 

of Tennessee CFD Lab academic research code [25] and 

the CFD Lab prototype production code jointly developed with JICS [26]. All codes are based theoretically on 

the pressure projection algorithm; the former two use a 

SIMPLE-variant finite volume discrete implementation, 

while the latter two employ the Williams finite element 

discrete implementation. The commercial and prototype 

production codes can handle both structured and unstructured meshes, constituted of unions of hexahedron 

and/or tetrahedron shaped cells. The academic research 

code is restricted to hexahedron meshes. 

EXPERIMENTAL RESULTS

Velocity field measurements

The sonic anemometer protocol recorded the time 

history of ventilation velocity vector distributions in the 

AERF cabin segment. Design of the Boeing 747 ventilation system relies on the “Coanda effect” (one-sided entrainment) to maintain supply wall jet attachment to the 

underside of the overhead luggage carrier until reaching 

the carrier terminus upward turn (Figure 4a). Thereat, 

the wall jet separates, forming a large circulation region 

spanning the entire cabin half-space. In that portion of 

the cabin segment containing both sidewall and central 

luggage carriers, a second circulation region is created 

above the main circulation, which is confined to the 

region between the two luggage carriers. Via uniformity 

of the supply and exhaust plenums, the AERF ventilation 

supply system was designed to induce minimal axial flow 

along the cabin axis. 

The orientation of the measurement planes with coordinate system is illustrated in Figure 2a). Three-dimensional 

velocity vector data were acquired on 10 planes transverse 

to the cabin axis spanned by the z-coordinate. At each 

transverse plane measurement location, the experimental 

recording rate was 3 samples/s over the 3-5 min recording 

window. The raw data were subsequently averaged over 

the four 30-s intervals, in the 3 – 5 min window, yielding totally time-independent cabin ventilation velocity 

vector field data sets. 
9

The composite perspective view of the cabin segment 

velocity measurement planes is presented in Figure 8. 

Only one plane of data is visible in the still image, which 

is representative of all plane data clearly visible in the 

data set animation. Planar presentation of the totally 

time-averaged velocity data at z-stations in the middle 

of cabin segment containing two luggage carriers, and 

at 9-in after termination of the central luggage carrier, 

is given in Figure 9 a)-b). The length of each vector 

corresponds to its magnitude, and the 2 m/s reference 

scale vector is present in each graphic near the supply 

plenum profile. 

Each z-station data set fully confirms the ventilation 

system design features. The separated wall jet clearly 

penetrates the cabin to the central luggage carrier, or 

cabin centerplane, creating a counter-clockwise circulation region filling the entire cabin half- span front to 

rear. A smaller, clockwise-rotating circulation region 

is generated in the cabin upper region bounded by the 

sidewall and central luggage carriers, where they both 

exist. The cabin circulation pattern does not exhibit 

significant z-dependence, as this measured component 

of the 3-D velocity vector was everywhere below instrument sensitivity, even in the near vicinity of the central 

luggage carrier terminus.

For experimental protocol validation, the complete 

AERF anemometer system was flown in a fully cabinconfigured Boeing 747-400 to measure select ventilation 

velocity vectors at cruise and altitude. Figure 10 presents 

the data sample coordinates, all of which are above seated 

head height, referenced to a CFD mesh of the AERF. The 

data acquisition rate was 60 hertz over a span of 5 min. 

Figures 11a)– e) graph the time evolution of these data, 

resolved over 1-s intervals into the unsteady mean flow 

and the rms variation about the determined mean. Each 

graph has superimposed a fifth-degree, running-smoothing polynomial of the resolution. The mean flow in each 

instance is time-varying, with period of order min, while 

the rms fluctuations appear time-invariant and essentially 

isotropic and homogeneous. 

The measured aircraft ECS-generated cabin ventilation 

velocity vector fields, in both the CAMI AERF and the 

Boeing 747-400 inflight test, led to a consensus summary 

containing the following observations:

The mean ventilation velocity vector field is time-varying with unsteadiness period on the order of a min.

Resolution of the 3-D 60 hertz velocity vector field 

measurements into time-unsteady mean and rms fluctuating components indicated the turbulence field is isotropic, 

homogeneous, and essentially time independent.

Figure 8. Perspective presentation of velocity vector experimental data planes in the 

AERF cabin segment devoid of seat rows.
10

Figure 9. Experimentally measured totally time-averaged ventilation velocity vector distributions, AERF cabin 

segment transverse planes without sear rows. a) (left) plane Z=86 containing both overhead luggage carriers, 

b) plane Z=226 with only the sidewall overhead luggage carrier.

Figure 10.Location of ventilation velocity vector sample points 

during Boeing 747-400 inflight cruise at altitude test, projected 

onto a planform of the AERF.
11

Figure 11a. Measured time-dependent ventilation velocity fields during inflight. Left column is timedependent, mean velocity vector scalar resolution; right column is rms fluctuating velocity vector scalar resolution. a) zone 1, b) zone 2, c) zone 5, d) zone 7, e) zone 14.
12

Figure 11b. Measured, time-dependent ventilation velocity fields during inflight 

experiment, left-column is mean velocity scalar resolution, right column is RMS 

fluctuating velocity scalar resolution (AJB graphic is corrupted, replace). 

Figure 11b. Measured, time-dependent ventilation velocity fields during inflight experiment; left-column is 

mean velocity scalar resolution, right column is RMS fluctuating velocity scalar resolution.
13

Figure 11c. Measured, time-dependent ventilation velocity fields during inflight experiment; left-column is 

mean velocity scalar resolution; right column is RMS fluctuating velocity scalar resolution.

Figure 11c. Measured, time-dependent ventilation velocity fields during inflight 

experiment, left-column is mean velocity scalar resolution, right column is RMS 

fluctuating velocity scalar resolution (AJB graphic is corrupted, replace). 
14

Figure 11d. Measured, time-dependent ventilation velocity fields during inflight 

experiment, left-column is mean velocity scalar resolution, right column is RMS 

fluctuating velocity scalar resolution (AJB graphic is corrupted, replace). 

Figure 11d. Measured, time-dependent ventilation velocity fields during inflight experiment, left-column is 

mean velocity scalar resolution; right column is RMS fluctuating velocity scalar resolution.
15

Figure 11e. Measured, time-dependent ventilation velocity fields during inflight experiment; left-column is 

mean velocity scalar resolution; right column is RMS fluctuating velocity scalar resolution.

Figure 11e. Measured, time-dependent ventilation velocity fields during inflight 

experiment, left-column is mean velocity scalar resolution, right column is RMS 

fluctuating velocity scalar resolution (AJB graphic is corrupted, replace). 
16

Conversely, the resolved time-varying mean velocity 

vector field is not isotropic or homogeneous and dependent on location within the cabin.

For the AERF ventilation rate of 40 ACH (air changes/hour), the mean velocity jet issuing from the supply 

plenum slot nozzle is order 5 m/s, which decays to order 

1 m/s just downstream of wall jet separation from the 

luggage carrier underside. 

After wall jet separation, the resultant free jet maintains coherence and momentum in traversing the cabin 

half-span distance, with the totally time-averaged mean 

velocity vector disposed to a slight upward arching.

The totally time-averaged mean flow speed within the 

cabin large circulation path decays from order 1 m/s to 

order 0.25 m/s. 

The nominal speed associated with time-average rms 

fluctuation velocity is order 0.025 m/s; the corresponding 

turbulence kinetic energy level k is order 0.01 m2/s2.

For nominal circulation path mean speed ranging 1 

- 0.25 m/s, the percentage of kinetic energy residing in 

the rms (turbulence) velocity field ranges 2 – 10 % .

Mass transport measurements

The design of the mass injection system was successful 

in not significantly altering the cabin ventilation velocity 

vector field (Figure 9). Therefore, data acquisition design 

called for measurement of VHP and/or water vapor concentrations along the nominal trajectory of the ventilation 

free jet traversing the upper cabin and continuing along 

the circulation path to the cabin floor. Figure 12 shows 

the VHP sample measurement locations on the platform 

of the cabin CFD solution domain. 

Figure 13 presents the spectrophotometer-measured 

raw data for VHP concentration at the first four sample 

locations along the free-jet trajectory as a function of time 

(min). On each graph, the dotted red line denotes the 

VHP generator being turned on and off. The diamond 

symbols correspond to each data point recorded at the 

system 10-s sampling interval. The solid blue line is a 30-s 

running-smoothing interpolation of the raw data. 

Viewing these four data sets, the VHP concentration level decreases along the cabin circulation path as 

expected. A steep rise in VHP concentration is recorded 

at the sample point nearest to wall jet separation, Figure 

13a), and a quite solid, steady-state level results with 

minimal data spread. In proceeding along the ventilation circulation path, the spread of the raw data about 

the running-smoothing mean monotonically increases 

as the onset rise progressively weakens. At the last two 

sample points nearest the central luggage carrier (Figure 

12), the raw data fluctuation spread is on the order of the 

mean, which makes the mean a less representative data 

summary. This issue totally dominates the data further 

along the cabin circulation path, so these sample-point 

data were not further considered.

DISCUSSION AND RESULTS

CFD velocity field prediction specifications

AERF cabin flowfield simulations were principally 

conducted using one commercial code [23] and the cited 

research CFD codes [25, 26] for a range of turbulence 

closure models. Select simulations were executed using 

a second commercial code [24] for cases encountering 

anomalous solution behavior. All computations were 3-

D on the described cabin symmetric half-geometry, as 

enabled by enforcing a symmetry boundary condition 

(BC) on the cabin mid-plane. The axial spans of the 

CFD solution domains ranged 0.3 - 5.0 m. All solution 

domain enclosing solid surfaces employed the no-slip 

BC for velocity. The AERF 40 ACH ventilation rate 

produces a nominal 5 m/s supply slot velocity magnitude; 

the corresponding Reynolds number per unit length is 

Re/L ≈ 1.5 E05/m, while Re ≈ 4 E03 is based on supply 

plenum data. 

For CFD simulations using the two-equation TKE 

turbulence closure model, the law-of-the-wall BC was 

employed. This approximation [16] replaces wall layer 

mesh resolution requirement with a boundary layer-based 

estimation of k and ε within a restricted distance from a 

solid surface. When using the one-equation closure model, 

the BC is eddy viscosity vanishing on solid surfaces. No 

special treatment for solid surfaces was requested as input 

specification for the commercial code LES models. These 

codes employ the simplest (box filter) LES implementation, and coalesce the four theory-generated Reynolds 

stress tensors into a single (dissipative only) tensor closed 

Figure 12.Transverse plane coordinates of 

sample locations for VHP measurements in the 

AERF.
17

Figure 13.Time evolution of VHP concentrations measured at sample 

locations in the AERF. Plane Z=108, a) X=074, Y=075, b) X=044, Y=072, c) 

X=028, Y=072, d) X=034, Y=064.
18

with the Smagorinsky subgrid scale (SGS) mixing length 

theory (MLT) eddy viscosity model. 

Simulations were also conducted using no turbulence 

model, i.e., laminar flow. Interestingly, the PDE form of 

laminar NS is identical to that produced by the LES box 

implementation, upon omitting the Smagorinsky closure 

model. All simulation CFD models, i.e., NS, RaNS, 

and LES, require stabilization generated by a numerical 

diffusion mechanism uniquely intrinsic to each code. 

Therefore, it is the interaction between numerical and 

physical model diffusion processes that ultimately leads 

to a solution. 

Baseline cabin CFD velocity field validation 

Baseline velocity field CFD simulations were conducted 

in the absence of the VHP injection system, on solution 

domains constituted of 1 to 9 axial mesh blocks. For vanishing derivative BCs specified on the associated domain 

axial end planes, all solutions generated flow fields that 

were numerically two-dimensional, as expected. Conversely, inclusion of the VHP injection system generates 

a local, modestly three-dimensional flowfield. 

For the laminar flow assumption at Re=4 E03, the 

commercial code [23] produced a steady solution using 

the first-order numerical dissipation option with execution time a matter of hours. The second- and third-order 

dissipation option continuations did not yield algorithm 

convergence. This first order laminar flow solution velocity field is summarized in Figure 14 as a color contour 

flood of speed. It exhibits good agreement with all essential aspects of the fully time-averaged experimental 

data (Figure 9a) as the Coanda-effect wall jet separates 

from the luggage carrier terminus and proceeds as a free 

jet across the entire cabin half-span, including a slight 

upwards arching.

The production research code [26] unsteady time-accurate laminar flow simulation, at Re=E03 and stabilized 

via its second order numerical dissipation model, generated a time-accurate solution through ~20 s elapsed time, 

comparatively requiring several days of computer time. 

This solution never achieved a steady state, the result of 

the wall jet separation at the luggage carrier terminus being time varying. Thereby, the free jet trajectory oscillated 

about the mean velocity unit vector pattern (graphed in 

Figure 15a) as uniform length (unit) vectors colored by 

speed. This presentation format enables visualization of 

the main cabin circulation pattern, filling the half-space, 

and the smaller clockwise circulation pattern bounded 

laterally by the luggage carriers. (This is preferred to the 

conventional vector presentation where vector length 

denotes velocity magnitude, as the graphic is overwhelmed 

by the length of slot jet vectors.) A magnified view of the 

slot jet region velocity distribution (Figure 15b) clearly 

shows smooth flow turning and acceleration through the 

Figure 14.Fluent generated ventilation velocity field in AERF 

cabin section. Z=86 with both overhead luggage carriers, 

steady laminar flow simulation, Re = 4 E 03, 3-dimensional 

velocity vector magnitude colored by speed. 
19

Figure 15.Transverse plane presentation of PICMSS generated ventilation velocity field in AERF cabin 

section, Z=86 with both overhead luggage carriers, unsteady laminar flow simulation, Re/L = E 03, a) 

velocity unit vectors colored by speed, b) magnification of velocity unit vectors in supply plenum-slot jet 

region.
20

slot, and this solution exhibits all essential aspects of the 

fully time-averaged experimental data (Figure 9a).

Using the TKE closure model and first-order numerical diffusion, the primary commercial code generated 

a steady velocity field distribution, again in a matter 

of hours. This TKE simulation did not predict wall jet 

separation at the luggage carrier terminus. Instead, the 

wall jet remained attached to the luggage carrier surface 

past the tangent break (recall Figure 2), hence it formed 

a single counter-clockwise circulation pattern reaching 

from cabin ceiling to floor. 

The desired separation location was forced by moving 

an axial mesh node row to create a “trip strip” at the luggage carrier tangent break. The resulting TKE solution, 

sequencing through the first- and second-order numerical 

dissipation options, produced the steady velocity vector 

field, graphed as unit vectors colored by speed in Figure 

16a). This solution also evidences essential agreement 

with the totally time-averaged data, including the slight 

upwards arching evident in the experimental free jet 

(Figure 9). Figure 16b) magnifies the slot jet region velocity distribution, while Figure 16c) is a highly magnified 

view of the mesh trip strip with local meshing density. 

The commercial code iterative process did not converge, 

for this TKE solution restarted using the third-order 

dissipation option.

Cabin mesh refinement CFD study

This result prompted a mesh refinement study focused 

on resolution of the luggage carrier terminus smoothly 

rounded profile, Figure 2. Figure 17a) graphs the mesh 

resolution improvement for the carrier curved terminus 

(red) and its sharp corner approximation (blue) resultant 

for mesh densification from 14,300 to 16,250 nodes per 

plane. Figure 17b) compares the sharp terminus (red) to 

a tangent approximation to the genuine curved surface 

(blue). The resultant commercial code second order dissipation option TKE solution transverse plane circulation 

patterns, colored by speed, are graphed in Figure 18. 

Both TKE solutions, for the genuine curvature and its 

tangent approximation, fail to predict flow separation 

at the luggage carrier terminus, leading to a single cabin 

circulation pattern. Conversely, the mesh with sharp 

corner approximation does lead to prediction of the 

correct separation phenomena, Figure 19, without the 

radical distortion originally used (Figure 16c). 

This result is fundamentally disturbing, as one basically 

must know the correct solution to generate the accurate 

CFD approximation! To test whether this is solely the 

attribute of the base commercial code, the second commercial code [24] was executed using the identical sharp 

and curved terminus refined meshes. Turbulence closure 

remains the standard TKE model with wall function BCs. 

The solution process was cycled through the suggested 

sequence of numerical dissipation options (Prelax=0.1, 

0.2, 0.3), for which the resultant velocity fields were 

visually indistinguishable. Figure 20 presents the luggage 

carrier rounded corner and sharp corner solutions, graphed 

in perspective as glyph velocity vector maps colored by 

speed, which fully confirms that the erroneous solution 

is not code-dependent. 

The requirement that the luggage carrier terminus 

be geometrically sharp is due to perhaps three attributes 

of these commercial code simulations. Primary may be 

use of the log-law approximation for TKE model BCs 

on the carrier, intrinsic to which is the assumption of an 

attached boundary layer flow. The replacement of the 

log-law BCs with no-slip BCs would require extensive 

mesh refinement throughout the entire wall jet region 

to accurately implement a low turbulent Reynolds 

number TKE model. This, in turn, would substantially 

increase mesh nodalization, with an attendant increase 

in computer execution time. A second contributing factor may be excessive numerical diffusion, although the 

suggested dissipation model cycling did not impact the 

erroneous solution generation. Finally, both commercial 

codes employ an iteration procedure designed to generate a steady solution, hence the selected numerical linear 

algebra process may be the dominating factor.

To evaluate these hypotheses, the academic research 

code executed a time accurate, unsteady TKE simulation 

on the original, single-width base mesh block (14,300 

nodes per plane). The mesh trip non-smooth node row 

(Figure 16c) was moved back to yield a sharp terminus, 

identical to that for which the commercial code simulation 

failed on this mesh. The TKE log-law BC definition was 

retained on the luggage carrier underside. The simulation 

initial condition (IC) was the commercial code steady TKE 

solution for the mesh with trip-strip (Figure 16a).

The velocity vector field immediately became time 

dependent, the result of wall jet separation unsteadiness from the luggage carrier terminus. Using the TKE 

log law BC, wall-jet separation remained located at the 

carrier terminus tangent break. Snapshots of the time 

evolution of transverse plane speed distribution over 40 

s of simulation time are graphed in Figure 21. The color 

spectrum is distorted to enhance visualization of the wall 

jet transition to the free jet in the near field (wherein red 

to yellow now appear). The short black lines are inserted 

lagrangian line segments that are carried by the velocity 

vector field as time evolves. Their motion further helps 

visualize the complexities of the resultant vortical flowfield 

generated by the time-dependent wall jet separation. 
21

Figure 16.Transverse plane presentation of Fluent generated ventilation velocity vector 

field in AERF cabin section. Plane Z=108 with both overhead luggage carriers, steady turbulent flow, TKE closure model, a) velocity unit vectors colored by speed, b) magnification 

of velocity unit vectors in supply plenum-slot jet region, c) mesh modification required for 

Coanda wall jet separation at luggage carrier terminus. Legend: b) Vmag in m/s, c)Speed, 

ranging from 0.5 –13.5m/s.
22

Figure 17.Transverse plane mesh density improvements in luggage carrier 

terminus region. a) genuine curvature compared to sharp corner approximation, b) tangent approximation to genuine curvature compared to sharp 

corner approximation.
23

Figure 18.Fluent generated transverse plane circulation patterns, AERF cabin 

section, plane Z=108 with both overhead luggage carriers, steady turbulent 

flow, TKE closure model. a) round carrier terminus mesh, b) mesh with tangent approximation to the round carrier terminus. Vmag in m/s.
24

Figure 19.Fluent generated transverse plane circulation pattern, AERF cabin 

section, plane Z=108 with both overhead luggage carriers, steady turbulent flow, 

TKE closure model, mesh with sharp corner approximation to the round carrier 

terminus. Vmag in m/s.
25

Figure 20.Star CD generated transverse plane circulation patterns, 

AERF cabin section, plane Z=108 with both overhead luggage carriers, steady turbulent flow, TKE closure model, a) round carrier terminus mesh, b) mesh with sharp corner approximation to the round 

carrier terminus.
26

Figure 21.Transverse plane presentation of a PSE generated solution in AERF cabin section, 

plane Z=108 with both overhead luggage carriers, time-accurate continuation from Fluent steady 

turbulent flow solution, TKE model, a) t = 0 s, b) t = 2 s, c) t =4 s, d) t = 6 s, e) t = 8 s, f) t = 10 s, 

g) t = 16 s, h) t = 22 s, i) t = 26 s, j) t = 30 s, k) t = 40 s. Legend: Vmag ranging from 0.5 – 6m/s.

a)

c)

e)

b)

d)

f)
27

Figure 21 (continued).

g)

i)

k)

h)

j)

l)
28

These CFD data confirm that the inability of the commercial CFD code solutions to correctly predict carrier 

terminus separation is not due to the TKE log-law BCs. 

Therefore, one can conclude that this problem stems 

from the direct steady-state iteration strategy using an 

inadequate mesh for the sharp corner luggage carrier 

terminus approximation. The cabin ventilation flowfield 

is clearly unsteady, as documented by the experimental 

data and this simulation, with wall jet separation oscillating about some mean as the motive cause. The unsteady 

CFD simulation further indicates that this unsteadiness 

penetrates upstream into the wall jet region itself. 

This prediction prompted probing of the AERF experimental data to assess the existence of natural frequencies. 

The 3-s data sample interval significantly compromises 

assessment fidelity, so it was completed only for the free 

stream horizontal velocity vector component at the data 

point closest to the carrier terminus. For the central two 

min during data recording, Figure 22 graphs the results. 

The experimental data are the solid symbols, the top 

curve is the Fourier interpolation, and the middle and 

bottom curves are the two predicted real frequencies in 

the interpolation. The resultant periods are the order 15 

and 30 s. The time-accurate CFD simulation predicted 

unsteadiness of the cabin ventilation flowfield is in solid 

qualitative agreement with this data analysis. 

Both commercial codes also state an LES simulation 

model capability. An LES time-accurate unsteady simulation was initiated using the base code, even though the 

mesh is not sufficiently refined to admit generation of a 

genuine LES simulation. The computation was terminated 

after three weeks’ elapsed execution time, which generated 

solution evolution through 12 s. Figure 23 summarizes the 

time evolution of the cabin ventilation flowfield graphed 

as streamlines of the transverse plane velocity field. The 

solution contains significant vortical content, in firm 

qualitative agreement with both the experimental data 

and the research code unsteady TKE simulation.

Baseline cabin mass transport CFD validation 

exercise

The key step is insertion of the mesh block containing 

the geometry of the VHP injector, hence quantification of 

alteration to the base empty cabin velocity field prediction. 

For the luggage carrier sharp terminus approximation, 

Figure 24 summarizes these results generated by the base 

commercial code. Figure 24a) repeats the data of Figure 

16a) graphed in the preferred transverse plane contour 

map colored by speed. Figure 24b) presents the corresponding distribution of turbulence Reynolds number, 

Ret, defined as the ratio of TKE model eddy viscosity to 

the kinematic viscosity of air. 

Figure 22.Fourier analysis of AERF experimental data. Cabin section plane Z=108 with both 

overhead luggage carriers, U_x velocity component, data coordinate closest to the luggage 

carrier terminus.
29

Figure 23.Transverse plane presentation of Fluent time accurate solution evolution in 

AERF cabin section. Plane Z=108 with both overhead luggage carriers, LES closure 

model, velocity streak lines at various times on 0 < t < 12 s.
30

Figure 24.Transverse plane distributions of Fluent generated solution in AERF cabin section. 

Plane Z=108 with both overhead luggage carriers, steady turbulent flow, TKE model, base 

mesh, a) velocity magnitude (speed) distribution, b) turbulent Reynolds number Ret distribution, 

TKE model; including VHP injector geometry mesh block, c) TKE model velocity magnitude 

(speed) distribution, d) turbulent eddy viscosity νt distribution, e) Spalart-Allmaras model velocity 

magnitude, f) turbulent eddy viscosity νt distribution. Legend: a), c), and e) show speed ranging 

from 0.5 – 13.5m/s; b), d), and f) show NuT ranging from 0 – 0.027.
31

Figures 24c) – d) present the companion commercial 

code TKE solution for the domain now including the 

VHP injector geometry mesh block but with the VHP 

supply flow set at 2% nominal to prevent backflow. The 

speed data distribution is sufficiently similar to the base 

mesh case to provide assurance that the VHP injector 

geometry would not drastically alter the ECS-generated 

AERF ventilation flowfield. Figure 24d) graphs the nonnormalized (by air kinematic viscosity) TKE eddy viscosity, 

and a modest visual distinction is apparent.

Also evaluated was base commercial code execution 

using the Spalart-Allmaras eddy viscosity closure model 

[21], which produced the steady solution data graphed 

in Figures 24e) - f). The cabin circulation trajectory 

identity is less pronounced, i.e., relatively more diffused, 

which is readily traced to the overall larger level of eddy 

viscosity generated by this closure model, compare Figures 24d) - f). 

All baseline CFD simulations confirmed the AERF 

cabin ventilation velocity vector field possessed a negligible 

axial component, even around the central luggage carrier 

termination. Since the VHP mass injection system was 

designed to be non-disturbing, with axial span of only 

0.1 m (4 in), it was deemed sufficient to employ the 

standard seat pitch span for the CFD solution domain. 

Figures 6 - 7 summarize the base geometry and resultant 

highly non-uniform, mesh-contained 1.2 million nodal 

points. Vanishing normal derivative BCs for all variables 

were applied on solution domain faces with unit normal 

aligned with the cabin axis. 

Figure 25 summarizes the commercial code TKE steady 

solution with the VHP mass injection system operating at 

nominal setting. The transverse plane speed map, Figure 

25a), is visually indistinguishable from the baseline flow 

computation without mass injection (Figure 16a). The 

notable distinction is a modest narrowing of both the wall 

and near field free jet contours due to entrainment of the 

injectant. The steady distribution of VHP in the cabin 

axial plane, containing the first experimental sample location (denoted 74,75 in Figure 12), possesses an essential 

mirror plane of symmetry, Figure 25b). The perspective 

3-D presentations of the steady distribution of VHP, 

Figures 25c)-d), confirm the absence of true symmetry, as 

induced by the VHP injection system terminus turning 

the injected flow through a 90° angle.

The key issue with mass transport simulation is time 

evolution of concentration distributions. In the genuine cabin environment, mass release would occur into 

the ECS-generated nominal ventilation flowfield. The 

experimental protocol employed this definition, which 

was best approximated for CFD experiments by using the 

cabin flowfield that included the (modest) steady injection velocity influence. Hence, the only solution process 

run time accurate was mass transport. The CFD experiment IC was mass fraction initialized within the entire 

injection piping system and zero everywhere within the 

cabin. The BCs were mass fraction fixed at the injection 

system inflow plane and vanishing normal derivative on 

all other solution domain surfaces.

The baseline CFD simulation was generated using 

the base commercial code operating time accurate for 

mass transport within the TKE-model generated steady 

velocity vector field. Figure 26 graphs the resultant time 

evolution of predicted mass fraction superimposed onto 

the experimental data for the four data coordinates 

closest to the free jet trajectory (recall Figure 12). The 

coordinate on the abscissa setting the superposition is 

the time of first appearance of measured and computed 

non-zero mass fraction, which of course is distinct for 

each data location. 

After extensive analysis, it proved impossible to set 

with confidence the experimental mass fraction level 

at the VHP injection face. Therefore, the Figure 26 

comparisons are normalized for CFD solution best approaching steady state fit with the experimental data 

moving average interpolations. Selecting this to occur 

at sample point (X044-Y072-Z108), Figure 26b) also 

yielded nominal matching at the sample point furthest 

from the free jet trajectory, Figure 26d). Consequently, 

substantial under-prediction of mass fraction level occurs 

at the closest sample point (X074-Y075-Z108), with 

over-prediction at the remaining sample point, Figures 

26a) and 26c). 

The Figure 26 comparisons generate more questions 

than answers. The CFD predictions obviously do not 

agree well with the data except at the set point. The 

severe under-prediction of the CFD level at the closest 

sample point is particularly concerning, as it could indicate the action of excess numerical or physical (TKE 

model) diffusion. One significant feature of agreement 

with this manipulation is the nominal identical slope of 

both the moving average and CFD solution curves at the 

time coordinate where VHP was first recorded at each 

data coordinate.

This observation led to a lagrangian particle transport 

CFD experiment to estimate the time frame of injectant 

circulation. Figure 27a) summarizes the time evolution of 

the coordinates of a lagrangian particle injected into the 

ventilation supply and VHP injection plenums. Being 

mass-less, these particles follow exactly the velocity field 

circulation trajectory into which they are placed. 

The key result is that both particles make a complete 

circulation of the cabin interior within 15 s elapsed time. 

For the VHP experiment 10-s sampling rate, the longer 
32

Figure 25.Fluent generated solution in AERF cabin section with VHP injection. Plane Z=108 with 

both overhead luggage carriers, steady- turbulent flow, TKE model, a) transverse plane velocity magnitude (speed) distribution, b) lateral plane mass fraction distribution containing sample point X=074, 

Y=075, c) 3-D perspective view of mass fraction distribution looking towards injector, d) 3-D perspective view of mass fraction looking down.

a) b)

c)

d)
33

Figure 26.Fluent generated solution for VHP time 

histories comparison with 

experimental data at four 

sampling coordinates, 

steady turbulent flow, TKE 

model, a) X074-Y075-

Z108, b) X044-Y072-Z108, 

c) X028-Y072-Z108, d) 

X034-Y064-Z108.

a)
34

Figure 27.Computed time history of VHP penetration and circulation in AERF cabin. 

a) Lagrangian particle track evolution for particles injected into ventilation supply and 

VHP supply, b) computed maximum mass fraction trajectory compared to experimental 

sample points. 

a)

b)
35

time data really record accumulation of VHP after multiple cabin circulations, hence accumulation. Thereby, 

the sole reliable data samples for VHP time-evolution 

assessment are only those acquired in the first 15 s of an 

experiment. Further, the viable data sample locations are 

only those discussed, the locations of which are compared 

to the CFD predicted trajectory of maximum VHP in 

Figure 27b). 

Mass transport fidelity CFD experiments

From the baseline mass transport assessment of VHP 

experimental data reliability, a sequence of CFD experiments were designed, conducted, and coded to add insight 

to CFD use issues that might facilitate extracting value 

from these data. Since the production research code 

cannot handle mixed tetrahedron-hexahedron meshes, a 

modification to the mass injection piping system (Figure 

7a) was made that enabled simulation using a purely 

hexahedron mesh. Thereby, the VHP circular piping 

system was replaced with a straight section of rectangular 

tubing that would have penetrated the cabin sidewall if 

actually installed in the AERF.

Figures 28a)-b) present perspective mesh blocks views 

of this modification, which not only eliminated the need 

for tetrahedron meshing but also enabled injectant BC 

application at a distance sufficient to develop a fully 

turbulent velocity field before entering the cabin. The 

axial span of the solution domain remained identical to 

the genuine geometry case, the local meshing remained 

highly non-uniform (Figure 28c-d), and the resultant 

domain mesh contained 952,041 nodes. 

The base commercial code with TKE closure model 

was executed on the revised problem geometry, producing a steady velocity field solution in a matter of hours. 

Visually, the resultant velocity and turbulent viscosity 

fields were essentially identical to those of the genuine 

geometry solution, compare Figures 29a)–b). The resultant steady mass fraction transverse plane distributions 

were also visually essentially identical. For a symmetry 

plane passed through the terminus of the genuine and 

modified injector geometries (Figures 29c–d) confirm 

the computed mass fraction distributions at 20 s elapsed 

time into the simulation are, for the present purpose, 

indistinguishable. 

With this assurance of simulation essential similarity, 

the commercial code steady velocity and turbulence fields 

were ported to the production research code. A matrix 

of computational experiments was executed to precisely 

assess the impact of TKE turbulence closure model and

numerical dissipation mechanisms on time evolution of 

mass fraction distributions. Figure 30 compares research 

code predictions of symmetry plane mass fraction distributions, after 20 s elapsed time, for zero turbulent and 

numerical viscosity, and input constant laminar flow 

binary diffusion coefficients of d = 0.64E-04 m^2/s (1.0 

in^2/s) and d = 0.64E-03 m^2/s (10.0 in^2/s). These 

selections for d generate solutions that under-predict and 

over-predict, respectively, the transverse plane diffusion 

of the mass fraction plume compared to the base commercial code TKE execution (Figure 29d). 

Thereby, the input constant binary diffusion coefficient 

d = 0.29E-03 m^2/s (4.0 in^2/s) produces a mass fraction 

solution, the penetration and lateral diffusion of which 

exhibits quite close agreement with the base commercial 

code TKE solution at t = 20 s, compare Figures 31a) and 

29d). Adding the TKE solution turbulent viscosity distribution to this input constant generates the mass fraction 

solution at t = 20 s (Figure 31b). Focusing on the light 

blue, different contours, the penetration of the plume is 

nominally halved, due to the TKE model-induced added 

level of diffusion. 

The subsequent CFD experiment tests the action 

of numerical diffusion on plume penetration, retaining use of the commercial code TKE simulation steady 

velocity field. An unsteady mass fraction simulation was 

executed using this code with its third-order numerical 

dissipation mechanism, the TKE diffusion term shut off 

and specifying the unrealistically small laminar binary 

diffusion coefficient d = 0.29E-05 m^2/s (0.04 in^2/s). 

The same data specification was executed using the 

prototype production research code with its numerical 

dissipation option turned off. Figure 32 compares the 

generated mass fraction distributions at t = 20 s, which 

clearly confirms that excessive numerical diffusion exerts a dominating influence on unsteady mass transport 

prediction fidelity.

The final CFD experiment tested the influence of time 

integration truncation error on baseline mass fraction 

solution. All solutions discussed to this point employed 

a time-step of ∆t=0.1 s using the implicit, second-order 

accurate non-dissipative trapezoidal rule integration 

algorithm. The production research code simulation 

for zero TKE diffusion and input constant binary diffusion coefficient d = 0.17E-03 m^2/s (2.5 in^2/s) was 

repeated using ∆t=0.05 s. Figures 33a)-b) graphically 

compare solution differences globally and locally about 

the VHP injection tube terminus. These data confirm 

that the truncation error associated with use of the standard time-step size is a negligible influence on the mass 

fraction solution everywhere except immediate to the 

injection tube terminus. Of note, the Courant number 

(non-dimensional time step) for the ∆t = 0.1 s solution 

ranges from C = 9.6 at the VHP injector to C = 90 at 

the ventilation supply slot.
36

Figure 28.Generated computational mesh of purely hexahedron-shaped cells for a segment of the 

AERF cabin without seat rows, VHP injection modified geometry. a) perspective view illustrating mesh 

block geometric essence, b) planar cross-section showing solution-adapted non-uniform meshing for 

local field resolution, c) modified VHP injection geometry mesh block, d) magnification of VHP injector 

local region meshing.

a) b)

c) d)
37

Figure 29.Transverse plane presentation of Fluent generated solution in AERF cabin section with 

VHP injection, plane Z=108 with both overhead luggage carriers, steady turbulent flow. a) genuine 

injector geometry, b) modified injector geometry, c) genuine geometry mass fraction distribution at t

= 20 s, d) modified geometry mass fraction distribution at t = 20 s.

a) b)

c) d)

a) b)

Figure 30.Transverse plane presentations of PICMSS generated VHP distributions in AERF cabin 

section, Z=108, modified geometry, steady TKE turbulent velocity field, t = 20 s, for constant mass 

binary diffusion coefficient. a) d = 1.0 in^2/s, b) d = 10.0 in^2/s.
38

Figure 31.Transverse plane presentations of PICMSS generated VHP distributions in AERF cabin section, 

Z=108, modified geometry, steady TKE turbulent velocity field, t = 20 s, constant mass binary diffusion coefficient d = 4.0 in^2/s. a) without TKE solution turbulent diffusion coefficient distribution, b) with TKE solution 

turbulent diffusion coefficient distribution.

a) b)

a) b)

Figure 32. Transverse plane presentations of CFD algorithm generated VHP distributions in AERF cabin section, Z=108, modified geometry, steady turbulent velocity field, t = 20 s, constant mass binary diffusion coefficient d = 0.04 in^2/s. a) Fluent solution with numerical diffusion, b) PICMSS solution without numerical diffusion.

a)

b)

Figure 33.Integration time step error assessment, PICMSS generated VHP mass transport in AERF cabin 

section, Z=108, modified geometry, steady turbulent velocity field, t = 20 s, constant mass binary diffusion coefficient d = 2.5 in^2/s. a) difference in 2 solutions on transverse plane, b) local magnification near VHP injection.
39

VHP temporal evolution CFD prediction

With an incisive understanding of the VHP injection 

experiment thus established, a set of CFD experiments 

sought to accurately quantify controlling aspects of the 

time-dependent simulation of VHP injectant distributions. Using the modified injection geometry mesh, the 

production research code was executed for a range of diffusion level specifications. The input steady velocity field 

remained that was generated by the base commercial code 

TKE solution, but the TKE eddy viscosity distribution 

was not used. The experiments employed the non-diffusive trapezoidal rule time integration algorithm, a zero 

level of numerical diffusion and input values for d, the 

laminar binary diffusion coefficient. 

Figure 34 graphs the production research code computed time-evolution of VHP mass fraction at each of 

the four experimental sample points lying close to the 

path of the ventilation free jet (recall Figure 12, for d = 

0.29E-03 m^2/s [4.0 in^2/s]). The time delay in appearance of VHP at each location is precisely indicated, 

as is the onset of VHP accumulation due to complete 

circulation around the cabin half-span. From these data, 

it is clear that the injectant front passes these first four 

sample points within 4 s elapsed time. Figure 35 graphs 

the t = 20 s z-axis distributions of VHP lateral spread at 

each of the four sample coordinates, just prior to circulation-induced VHP addition at the first sample point. 

Each of these contours exhibits the expected Gaussian 

distribution. 

Figure 36 summarizes the comparative results of the 

range of CFD experiments predicting time evolution of 

the VHP mass fraction level at the four prime experimental data points. These data are normalized to match 

the first non-zero experimental value at the third sample 

point (028,072,108) (recall Figures 12 & 26). The three 

experimental data symbols on each graph are the zero 

Figure 34.Time evolution of VHP mass fraction at 4 experimental data coordinates, PICMSS 

time-accurate mass transport solution in AERF cabin section, Z=108, modified geometry, 

steady turbulent velocity field, constant binary diffusion coefficient d = 4.0 in^2/s, 0 < t < 20 s.

Figure 35.Lateral direction VHP mass fraction distributions at 4 experimental data coordinates, PICMSS 

time-accurate mass transport solution in AERF cabin 

section, Z=108, modified geometry, steady turbulent 

velocity field, constant binary diffusion coefficient d = 

4.0 in^2/s, t = 20 s.
40

Point 74,75,0

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

05 10 15 20 25

Time (s)

Concentration (mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent -

SQR Tube

Experiment

Fluent -

RND Tube

a)

Point 44,72,0

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

05 10 15 20 25

Time (s)

Concentration (mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent -

SQR Tube

Experimen t

Fluent -

RND Tube

b)

Point 28,72,0

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

05 10 15 20 25

Time (s)

Concentration (mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent -

SQR Tube

Experiment

Fluent -

RND Tube

c)

Point 34,64,0

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

05 10 15 20 25

Time (s)

Concentration (mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent -

SQR Tube

Experiment

Fluent - RND

Tube

d)

Figure 36.Compari-

son of time evolution 

of VHP mass frac-

tion levels to data 

at 4 experimental 

data coordinates, 

PICMSS and Fluent 

time-accurate mass 

transport solutions in 

AERF cabin section, 

modified geometry, 

steady turbulent 

velocity field, TKE 

and various con-

stant binary diffusion 

coefficients, data 

matched at coordi-

nate (028, 072, 108), 

0 < t < 20 s.
41

X074-Y075-Z108

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25

Time (s)

Concentration

(mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent - SQR

Tube

Experiment

Fluent - RND

Tube

X044-Y072-Z108

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 5 10 15 20 25

Time (s)

Concentration (mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent - SQR

Tube

Experiment

Fluent - RND

Tube

X034-Y064-Z108

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0 5 10 15 20 25

Time (s)

Concentration 

(mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent - SQR

Tube

Experiment

Fluent - RND

Tube

X028-Y072-Z108

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 5 10 15 20 25

Time (s)

Concentration

(mg/l)

DIFF=1.0

DIFF=4.0

DIFF=10.0

Fluent - SQR

Tube

Experiment

Fluent - RND

Tube

a)

b)

c)

d)

Figure 37.Comparison of time evolution of VHP mass fraction levels to data at 4 experimental data coordinates, PICMSS and Fluent time-accurate mass transport solutions in AERF 

cabin section, modified geometry, steady turbulent velocity field, TKE and various constant 

binary diffusion coefficients, data matched at coordinate (044, 072, 108), 0 < t < 20 s.
42

Figure 38.Solution domain for CFD simulation of ventilation flow in the fully configured 

AERF cabin. a) (left) axial view of the half-span double seat row solution domain block with 

gasper, supply and exhaust plenums fully visible, b) transverse view showing axial span 

mesh block termination transects on seat backs.

Figure 39.Computational mesh for CFD simulation of velocity field and mass transport in the fully configured AERF cabin. a) (left) axial perspective view of mesh density, b) (upper right) close-up of gasper 

plenums, c) (lower right) seat surface mesh density.
43

Figure 40.Fluent generated ventilation velocity vector field stream tubes, colored by 

speed, in fully configured AERF cabin section with both overhead luggage carriers, mesh 

with carrier trip strip, steady turbulent flow, TKE closure model. a) (left) axial perspective, 

b) transverse perspective.

Figure 41. Star CD generated ventilation velocity vector field stream tubes, colored by speed, in 

fully configured AERF cabin section with both overhead luggage carriers, mesh with carrier trip strip, 

steady turbulent flow, TKE closure model. a) (left) axial perspective, b) transverse perspective.
44

Figure 42.Star CD generated gasper vector stream tube evolution, colored by gasper, in fully configured 

AERF cabin section with both overhead luggage carriers, mesh with carrier trip strip, steady turbulent flow, 

TKE closure model.
45

Figure 43.Star CD generated gasper vector stream tube evolution, colored by gasper, non-vertical gasper 

plenum orientations, in fully configured AERF cabin section with both overhead luggage carriers, mesh with 

carrier trip strip, steady turbulent flow, TKE closure model.

Figure 44.BE 

Aerospace concept for introduction of irradiated 

gasper air flow. 

a) (top left) treatment system 

in passenger 

control unit, b) 

(lower left) face 

of passenger 

control unit, c) 

(right) gasper 

flow visualization 

experiment. 
46

reading, preceding the first and second non-zero readings, 

each of which is separated by the 10-s experimental data 

rate. The input laminar binary diffusion coefficients d for 

the research code are therein denoted “DIFF,” while the 

commercial code TKE executions are graphed for both the 

genuine (RND) and modified (SQR) injection geometries. 

Very poor correlation with the experimental data accrues 

to this normalization at all but the set point. 

Figure 37 recreates this comparison for the CFD 

predictions normalized at the first non-zero experiment 

value at the second sample point (044,072,108). On all 

except the set-point graph, the smallest laminar binary 

diffusion coefficient solution is furthest from the data, 

as expected. The commercial code solutions for both 

genuine and modified injection geometries, and those 

generated by the research code for mid-range binary 

diffusion coefficients nominally overlay each other at the 

two mid-trajectory experimental locations. At the furthest 

downstream trajectory data station, these binary diffusion 

specification simulations produce solutions spanning the 

experimental data, while the commercial code solutions 

lie closest to the 10 s data point.

Fully configured cabin velocity field CFD 

experiments

All measurements and CFD assessments reported to 

this point were performed for the AERF devoid of seat 

rows. The original plan was to continue with experiments 

in the AERF fully configured with seat rows and to assess 

the impact of gasper operation, the design of which was 

to be guided by the results of CFD predictions. These 

precursor CFD simulations have been completed, yielding 

quantitative prediction of alterations to AERF ventilation flowfield distributions anticipated to be measured 

experimentally.

The CFD solution domain transverse span remains 

the half-cabin, with the longitudinal span containing 

the equivalent of two seat rows. Perspective views of the 

associated solution domain are given in Figures 38a)-b). 

Note that the fore- and aft-domain planes transect the 

seat row back, the location of which was a specifically 

selected application of vanishing normal derivative BCs, 

as appropriate for the assumption of negligible cabin axial 

velocity component. The cabin ventilation supply and 

exhaust plenums are clearly visible (Figure 38a), as are 

the plenums for the three gaspers, shown therein.

This CFD solution domain was discretized into a 

non-uniform mesh of purely hexahedral cells containing 

the order 2.25 million nodes, fully blocked for parallel 

computing. The transverse plane nodal density in the 

wall-free jet region, Figure 39a), emulates that of the mesh 

refinement study. Since gasper operation corresponds to 

the classic jet in crossflow, further nodal densification was 

added in regions where gasper flows interact with the wall 

jet. Figure 39b) presents a magnified view of the internal 

geometry and meshing in the gasper plenum terminus 

– wall jet region. Finally (Figure 39c) illustrates mesh 

density on the surface of the seat rows. 

Two variations of this mesh were generated, one with 

luggage carrier sharp terminus and the second with the 

tangent approximation to its genuine curvature, recall 

Figure 16. As expected, the steady TKE model CFD 

solutions generated by both commercial codes suffered 

the error detected for the empty cabin simulation, i.e., 

generation of a single circulation region due to failure of 

the wall jet to separate at the luggage carrier terminus. 

For the sharp carrier terminus, both commercial code 

TKE solutions predict wall jet separation in agreement 

with the totally time-averaged experimental data, hence 

produce cabin dual circulation patterns. For two perspective orientations, Figures 40a)-b) and 41a)-b) summarize 

these steady velocity field solutions in terms of flow 

stream tubes colored by speed. There exist no surprises in 

these solutions, each of which predicts anticipated flow 

deflection by the seat backs and generation of additional 

tubular circulation regions between the seat undersides 

and the cabin floor.

CFD simulation of the operation of the gaspers with 

the ventilation supply flow concludes this project component. The nominal exit velocity for a full-open gasper 

is order 15 m/s, nominally an order of magnitude larger 

than that of the local wall jet, through which each must 

penetrate. Nevertheless, the CFD simulation results confirm that significant deflection of the gasper flows occurs 

in penetrating the wall jet. Figures 42a)-d) presents time 

evolution of the stream tube trajectory of the three gasper 

jets, color-coded for origin, for the direction of injection 

vertical. It is clear that the first jet suffers the greatest 

deflection from vertical, and that each successive gasper 

jet is accordingly shielded by its predecessor. 

Of course, gasper flow direction is individually adjustable by each seated occupant, so is readily redirected 

to the correct seat location. Figures 43a) – d) illustrate 

the results of successively adjusting each gasper plenum 

chamber axis from the vertical, such that the gasper flow 

trajectories are now nominally parallel. 

Fully configured cabin mass transport CFD 

experiment 

A Cooperative Research and Development Agreement 

was established between the FAA CAMI and BE Aerospace to evaluate their concept of a rapid air exchange 

response system, should a toxic substance released into 

an aircraft cabin be detected. The BEA innovation involved a modification to the cabin gasper plenum system 

in each overhead passenger control unit. The concept 
47

was to rapidly generate a “good air envelope” over each 

seated passenger via subjecting the gasper airflow to 

strong ultra-violet radiation prior to issuing it from the 

control unit. Figure 44a)-c) illustrates the design essence 

of this system. 

The key characterization sought was estimation of the 

time evolution of the gasper flow envelope as directed 

towards a seated occupant. The associated AERF experiment was designed and some initial data recorded, but 

the experimental study was not completed. For the cabin 

ventilation flow at the steady TKE solution, shown in 

Figure 41, the results of a cursory CFD experiment are 

summarized in Figure 45 as time evolution of the envelope 

of mass emanating from the center gasper in the AERF 

passenger control unit. In the absence of a seated mannequin to deflect the flow, the gasper is directed to the 

seat back. Within 1 sec, the contour of 5% treated air 

touches the seat back and thereafter does not penetrate 

significantly further. The contour of 1.5% treated air 

envelopes the seat front and back by 2 s; thereafter, these 

solution profiles remain essentially unchanged through 7 

s of simulation time. No further CFD experiments were 

conducted, as project funding drew to a close. 

SUMMARY, CONCLUSIONS, AND 

RECOMMENDATIONS 

Summary

The aircraft ECS-generated cabin ventilation velocity vector field CFD validation exercise was enabled by 

acquisition of high-quality, time-accurate 3-dimensional 

experimental data for the cabin velocity vector distribution 

in the Boeing 747-100 AERF and a Boeing 747-400 in 

flight. These data clearly confirm and quantify the existence of a time-dependent, turbulent velocity vector field, 

constituted of a large-cabin proper circulation pattern 

and a second smaller circulation region bounded by the 

luggage carriers. This flowfield is energized by unsteady 

Coanda-effect wall jet separation from the underside of 

the cabin sidewall luggage carrier curved terminus. 

For two commercial codes, the TKE closure model 

steady solution process is exceptionally efficient in 

generating a time-independent, velocity-vector field. 

Conversely, a time-accurate, CFD simulation solution 

process appears required, which is much more demanding on computer resources. The combined use of commercial and proprietary CFD codes did yield qualitative 

and quantitative assessments of factors influencing CFD 

simulation velocity vector field fidelity. 

The disturbing factor with the commercial code TKE 

steady solutions is that qualitative agreement with the 

completely time-averaged experimental data occurred only 

for the insertion of a tangent discontinuity at the luggage 

carrier terminus. The mesh refinement study confirmed 

that, with sufficient mesh resolution to accurately define 

the correct carrier terminus, the wall jet did not separate 

but, instead, remained attached, yielding a single cabin 

circulation pattern at odds with the data. The requirement for use of a mesh “trip strip” appears dominated 

by the steady flow iteration strategy employed by these 

codes. The true value of such a rapidly generated TKE 

solution appears as an initial condition for a following 

time-accurate, unsteady CFD simulation. Specifically, 

the time-accurate research code CFD simulation generated a time-dependent solution in good qualitative and 

promising quantitative agreement with the experimental 

data, including a frequency analysis. 

The validation exercise for mass transport aircraft 

generated results that were challenging to interpret but 

were, nevertheless, very informative. The dominant contributing factor was experimental data unreliability, the 

result of the sampling rate being too slow in comparison 

with the circulation time within the cabin. This issue was 

further complicated by the overall ventilation flowfield 

unsteadiness, which resulted in experimental data fluctuation spread on the order of the mean, therefore the mean 

is a non-representative data summary. The inability to 

firmly quantify VHP mass fraction in the injection stream 

entering the cabin was an additional challenging factor. 

Nevertheless, the combination of commercial and research 

code CFD experiments did generate clear assessments of 

the balance between flow convective-diffusive and CFD 

algorithm/code dissipative mechanisms, both physical 

and numerical, in predicting of the time evolution of 

mass fraction distributions in the AERF.

Conclusions

The ECS-generated aircraft cabin ventilation velocity vector field CFD validation exercise is deemed very 

successful. A firm knowledge base now exists for characterizing factors affecting the fidelity of time-accurate, 

3-dimensional CFD predictions of ventilation velocity 

vector flowfields in the AERF. The established sonic 

anemometer experimental data protocol is confirmed 

to be highly appropriate in supporting the validation 

requirement. 

The validation exercise for CFD prediction of mass 

transport was less conclusive, due principally to limitations inherent in the experimental measurement protocol. 

Notwithstanding, specifically designed and executed 

computational experiments did provide clear insight 

into mechanisms dominating CFD time-dependent, 

mass transport simulation fidelity, hence design of an 

improved experimental protocol.
48

Figure 45.Star CD generated gasper flow envelope evolution, central gasper only operating in fully 

configured AERF cabin section with both overhead luggage carriers, mesh with carrier trip strip, 

steady turbulent flow, TKE closure model, yellow contour at 5%, blue contour at 1.5 %, time interval 

1 < t < 7 s.
49

The CFD computer demand distinction between the 

commercial steady RaNS codes and select time-accurate 

algorithms/codes is clearly a dominating factor in mass 

transport simulation in aircraft ventilation flowfields. 

Combining these capabilities appears to be the required 

approach, with steady code results, properly interpreted 

and generated, producing initial conditions for the ensuing 

time-accurate mass transport CFD simulation. 

Recommendations

The CAMI AERF is an exceptionally valuable research 

facility to support research on the fate of pathogens introduced into aircraft cabin ventilation flowfields. The 

requirements are clearly delineated for the acquisition 

of quality velocity and mass transport experimental data 

to support CFD validation requirements. The astute 

use of CFD methodology can certainly generate precise 

design guidance for such experiments, effectively and 

efficiently reducing the size of the data matrix required 

to serve the validation requirement. The results of this 

steadily growing knowledge base will predict estimation 

of optimal opportunities for onboard sensor locations, 

hence will support associated hypotheses for examining 

potential damage mitigation strategies, should a cabin 

release event be detected.

REFERENCES

1. Kelso, R. M., Roy, S. & Baker, A. J., “CFD Prediction of Thermal Comfort Distributions in a 3-D 

Space With High Ventilation Rates,” in Seppanen, 

O. et al. (eds), Indoor Air ’93: 6th Int. Conf. on 

Indoor Air Quality and Climate, V. 5, Helsinki 

Tech. Univ. Press, Finland, p. 313-8, 1993.

2. Nielsen, P.V., “Prediction of Air Flow and Comfort 

in Air Conditioned Spaces,” ASHRAE Transactions, 

V. 81, Part 2, 1974.

3. Lemairc, A. D., Ed, Room Air and Contaminant Flow 

Evaluation of Computational Methods, Summary, 

International Energy Agency, Annex 20, 1992.

4. Moser, A, Director, International Energy Agency 

Annex 26: Energy Efficient Ventilation of Large 

Enclosures, ETH Zurich, 1992.

5. Baker, A. J., Williams, P.T., & Kelso, R. M. , 

“Numerical Calculation of Room Air Motion, 

I. Math, Physics and CFD Modeling,” ASHRAE 

Transactions, V.100, Pt. 1, p.514-30, 1994.

6. Williams, P.T., Baker, A. J., & Kelso, R. M. , 

“Numerical Calculation of Room Air Motion, II. 

the Continuity Constraint FE Method for 3-D 

Incompressible Thermal Flows,” ASHRAE Transactions, V.100, Pt. 1, p.531-48, 1994.

7. Williams, P.T., Baker, A. J., & Kelso, R. M. , 

“Numerical Calculation of Room Air Motion, III. 

3-D CFD Simulation of a Full Scale Room Air 

Experiment,” ASHRAE Transactions, V.100, Pt. 1, 

p.549-64, 1994.

8. Williams, P. T. & Baker, A. J., “CFD Characterization of 3-D Natural Convection in a Two-Cell 

Enclosure with a Door,” ASHRAE Transactions, V. 

100, Pt. 2, p.685-96, 1994.

9. Murakami, S. Kato, S, & Kondo, Y., “Numerical 

Prediction of Horizontal Non-Isothermal 3-D Jet 

in Room Based on Algebraic 2nd Moment closure Model,” ASHRAE Tech Paper, AN-92-9-3, 

1992.

10. Baker, A. J., Winowich, N.S., Taylor, M.B. & Heller, 

M. R., “Prediction of the Distribution of Indoor 

Air Quality and Comfort in Aircraft Cabins Using 

Computational Fluid Dynamics (CFD),” in N.L. 

Nagda, ed. Air Quality and Comfort in Airliner 

Cabins, ASTM SP 1393, p. 117-34, 2000.

11. Jones B.W., Hosni, M..H., & Meng, H., “The 

Interaction of Air Motion and the Human Body 

in Confined Spaces,” ASHRAE Research Project 

978 Final Report, 2001.

12. Ericson, S. C., Wong, K.L. , Orzechowski, J. A., 

Baker, A. J., Garner,R. P. & McCloskey, T. S., “CFD 

Validation for Contaminant Transport in Aircraft 

Cabin Ventilation Flowfields,” Proceedings, 7th 

Intl. Sym. on Ventilation for Contaminant Control,

Sapporo, Japan, 2003.

13. Baker, A. J., Roy, S. & Kelso, R. M., “CFD Experiment Characterization of Airborne Contaminant 

Transport for Two Practical 3-D Room Air Flowfields,” Building. & Environment, V. 29, p. 253-59, 

1994.

14. Memarzadeh, F., Assessing the Efficacy of Ultraviolet 

Germicidal Irradiation and Ventilation in Removing 

Mycobacterium Tuberculosis, National Institutes of 

Health (NIH) Handbook, 2000.

15. Baker, A. J., Kelso, R. M., Gordon, E. B., Roy, S., 

& Schaub, E.G., “Computational Fluid Dynamics: 

A Two-Edged Sword,” ASHRAE Journal, V. 39, No. 

8 , p. 51-8, 1997.
50

16. Williams, P. T. & Baker, A. J., “Incompressible 

Computational Fluid Dynamics and the Continuity Constraint Method for the 3-D Navier-Stokes 

Equations,” Journal of Numerical Heat Transfer, Part 

B, V29, p.137-273, 1996.

17. Chaffin, D. J. & Baker, A. J., “On Taylor Weak 

Statement Finite Element Methods for Computational Fluid Dynamics,” International Journal 

of Numerical Methods in Fluids, V. 21, p.273-94, 

1995

18. Kolesnikov, A. & Baker, A. J., “An Efficient High 

Order Taylor Weak Statement Formulation for the 

Navier-Stokes Equations,” Journal of Computational 

Physics, V. 173, p. 549-74, 2001.

19. Wilcox, D. C., Turbulence Modeling for CFD, 2nd

Ed, DCW Industries, 2000.

20. Pope, S. B., Turbulent Flows, Cambridge University 

Press, 2000.

21. Spalart, P.R. and Allmaras, S.R., “A One-Equation 

Turbulence Model for Aerodynamic Flows,” AIAA 

Technical Paper 92-0439, 1992.

22. Moin, P. & Mahesh, K., “Direct Numerical Simulation: A Tool for Turbulence Research,” Annual 

Review of Fluid Mechanics. V30, p.539-78.

23. Fluent, Inc. CFD Flow Modeling Software and 

Services. [www.fluent.com]. 12/8/2004.

24. CD-adapco, Inc. Star-CD Flow Modeling Software 

and Services. [http://us.adapco.com]. 12/8/2004.

25. Wong, K.L. A Parallel Interoperable Computational 

Mechanics System Simulator (PICMSS). [www.

picmss.org]. 12/8/2004.

26. PSE CFD Lab Academic Research CFD Code. 

[http://cfdlab.utk.edu/Internet courses/ES552w/

PSEs]. 12/8/2004.

27. Campbell Scientific, Inc. Wind Speed and Wind 

Direction Sensors. 2/17/2004. [www.campbellsci.

com/wind.html#csat].12/20/2004.

28. Thomas, Jim, “FAA/CAMI On-Site Testing in a 

747 Fuselage Section, Project 81051.” Strategic 

Technology Enterprises Technical Report, April 

2004.

29. Guided Wave Incorporated. H2O2 Monitor. 

[www.guided-wave.com/h2o2_monitor.htm]. 

12/20/2004.

30. National Instruments Corporation. LabVIEW 

– The Software That Powers Virtual Instrumentation – Products and Services. 2004. [www.ni.com/

labview]. 12/8/2004.