2011 Proceedings Vol. 10 Part 1

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Woodruff School of Mechanical Engineering

Georgia Institute of Technology

Atlanta, Georgia, USA

November 4-5, 2011

Volume 10

Driving Force

Market D namics


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ASME DISRICT F, EARLY CAREER

TECHNICAL JOURNAL

Volume 10

Presented at

ASME District F, Early Career Technical Conference

Woodruff School of Mechanical Engineering


Atlanta, Georgia

November 4-5, 2011

Sponsored by

The ASME Old Guard


HOLTEC International

Unified Brands

ASME District F

ASME Atlanta SectionASME Birmingham Section

ASME Mississippi Section

Technical Committee

Dr. J. Donnell

Dr. P. Durbetaki

Dr. S. Jeter

Chair of Coordinating CommitteeDr. K. R. Rao

For more information about ECTC 2011 visit: http://districts.asme.org/DISTRICTF/ECTC/

THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS SOUTHEAST DISTRICT F

THREE PARK AVENUE, NEW YORK, NY 10016

iii


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Copyright 2011 by ASME Southeast District F, Three Park Avenue,New York, NY 10016

All rights reserved. Printed in the United States of America. Except as permitted under the

United States Copyright Act of 1976, no part of this publication may be reproduced or

distributed in any form or any means, or stored in a data base or retrieval system, without

the prior written permission of the publisher.

ASME shall not be responsible for statements or opinions advanced in papers or printed in

its publications (B7.1.3). Statement of ASME Bylaws.

ASME District F encompasses the states of Alabama, Delaware, District of Columbia,

Florida, Georgia, Maryland, Mississippi, North Carolina, South Carolina, Tennessee and

Virginia. For more information go tohttp://districts.asme.org/DistrictF.htm.

ISBN 978-1-4507-9223-3

ASME 2011 Early Career Technical Journal - Vol. 10 iv
http://districts.asme.org/DistrictF.htmhttp://districts.asme.org/DistrictF.htmhttp://districts.asme.org/DistrictF.htmhttp://districts.asme.org/DistrictF.htm


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FOREWORD

Journal of ASME District F Early Career Technical ConferenceNovember 4 5, 2011

One of the primary objectives of American Society of Mechanical Engineers (ASME) is the dissemination of

technical information. Pursuant to this goal the ASME Southeastern Region (Region XI) initiated the

Graduate Student Technical Conferences (GSTC) in 1991 when Dr. P. Durbetaki was the Vice President of

Region XI; it was held in the School of Mechanical Engineering, Georgia Institute of Technology, Atlanta,GA. The Regional Technical Conference (RTC) was initiated in 2001 by the Regional Vice President Dr. K.

R. Rao and with the help of the Region XI Operating Board.

The first RTC was held on April 6, 2002, in Jackson, Mississippi with the participation of students across all

five states (Florida, Georgia, Alabama, Tennessee and Mississippi) of the former ASME Region XI. The

quality of the reviewed papers, published in the four volumes of the Regional Technical Journal from 2002through 2006, called for this Conference to be renamed in 2006 as the Early Career Technical Conference

(ECTC) indicating the changing demands of the times. On October 6 and 7, 2006 ECTC was held, in

Jackson, MS with a robust support of Entergy Operations. Inc. In 2007, for the first time, ECTC was held ina University, Florida International University (FIU), in Miami, Florida. ECTC 2008 was also held at FIU,

Miami, Florida with invitations being extended to all of the Universities invited for ECTC 2007. Dr. Yong

Tao was the Chair, Technical Committee and Editor for ECTC 2007 and Dr. Sabri Tosunoglu for ECTC

2008. The generous financial support of ASME Old Guard funded the registration and even offset a part ofthe travel expenses of the paper presenters attending these conferences.

The success of the preceding ASME District F Early Career Technical Conferences prompted the organizersto look beyond the frontiers of the District. For the current ECTC 2010 invitations were sent to all of the

universities affiliated with all of the ASME Districts A to J, that were even beyond USA. It is gratifying to

mention that we reached the cap of 35 paper presenter entries that we targeted. Of these we have 14submittals from outside USA. Thus, the theme for this Conference ECTC Opens the Window to Outside

USA has been amply demonstrated! The Department of Mechanical Engineering of the University of

Alabama at Tuscaloosa, Alabama hosted the ECTC 2009. The Editor of the ECTC 2009 Journal establishedan elaborate review process similar to the process used by ASME Technical Divisions. Papers weredistributed to Associate Editors of the Editorial Board based on their areas of expertise, and they obtained

reviews for each paper by expert reviewers in the field. The Editorial Board made a significant effort to

ensure that the review process for each paper followed the criteria and deadlines. Because of this rigorousrequirement, the papers submitted were accepted as either for presentation only at the conference, or for both

presentation and journal publication. Authors whose papers were rated acceptable for publication in the

Technical Journal were required to make corrections and enhancements, based on reviewers comments.

The Woodruff School of Mechanical Engineering of the Georgia Institute of Technology in Atlanta, Georgia became

the host for the ECTC 2011. The review and selection process initiated with ECTC 2009 and continued with ECTC

2010, was followed for the ECTC 2011 Conference.

This ECTC 2011 Journal is a compilation of twenty-three (23) reviewed and accepted technical papers from USA,England and Asia.

K. R. Rao, PhD, PE., Fellow ASME, FIE, CEChairECTC Coordinating Committee

P. Durbetaki, PhD, Life Fellow ASMECo-ChairECTC Coordinating Committee

ASME 2011 Early Career Technical Journal - Vol. 10 v


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ACKNOWLEDGEMENTS

ASME District F Early Career Technical JournalNovember, 2011

Many individuals contributed to the success of this Early Career Technical Conference and this

ECTC 2011 Journal.

The Coordinating Committee express gratitude and appreciation to Dr. K R. Rao, Chair, Dr. P.

Durbetaki, Co-chair of the Conference Coordinating Committee, and members of the Conference

Committee Dr. Bill Wepfer, Dr. Sheldon Jeter, Dr. Jeffrey Donnell, Michael D. Stewart, Dr.

Yong Tao and John Mulvihill for their tireless and dedicated efforts in organizing theconference, directing the review process and overseeing the presentations. Their hard work has

made this journal possible.

ECTC 2011 Journal Editorial Board comprises of theEditors, Dr. J. Donnell, Dr. P. Durbetaki

and Dr. S. Jeter. Like the previous years ECTC 2009 and ECTC 2010, the ECTC 2011 had

separate tiers of Associate Editors from Faculty and from Professionals. We thank all themembers of the Editorial Board and invited reviewers, who spent countless hours of their own

time reviewing and critiquing the papers to ensure a quality publication. The advice and

guidance of Dr. K. R. Rao in the preliminary set up of the web site, establishment of the finalweb site, revisions of the web site as needed, as well as with reviews and editorial matters is

hereby acknowledged and appreciated. Dr. Yong Tao is acknowledged and appreciated as web

master for ECTC 2011; his help is very much recognized.

ASME District F and the Coordinating Committee thank ASME staff, Christina Perakis, JessicaAlbert, Deidra L. Hackley, and Nicole Alston for the administrative support to the success of the

conference and the publication of this journal.

Finally, we all owe a great debt of gratitude to our principal sponsors Woodruff School of

Mechanical Engineering of the Georgia Institute of Technology, the ASME Old Guard,HOLTEC International, Unified Brands, ASME District F, ASME Mississippi Section, ASME

Atlanta Section and ASME Birmingham Section, and Faculty Support from the Faculty of

Georgia Tech, ME.

-- 2011 ASME ECTC Coordinating Committee --

ASME 2011 Early Career Technical Journal - Vol. 10 v


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The ASME Early Career Technical Conference Committee and

Technical Journal Editorial Board Express their Appreciation

to the Following Sponsors for SupportingECTC 2011

WWooooddrruuffffSScchhoooollooff

MMeecchhaanniiccaallEEnnggiinneeeerriinngg

Georgia Institute

ofTechnology

ASME DISTRICT F

ASME ATLANTA SECTION

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ASME 2011 Early Career Technical Journal - Vol. 10 vii


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Table of Contents

INSIDE COVER iii

FOREWORD v

ACKNOWLEDGEMENTS vi

SPONSORS vii

1. MOHAMMED ALIand ESSAM A. IBRAHIM 1Computational Investigation of Particle Settling Effects on Inhaled Submicron Bioaerosol Deposition in the Human Lung

2. THOMAS M. STONE, SEUNG-KYUM CHOI, PhD andHEMANTH AMARCHINTA 6Dynamic Modeling and Parameter Estimation for Impact Analysis and Robust Packaging Design

3.

POONAM SAVSANIand VIMAL SAVSANI 14

Motion Tracking Through Machine Vision

4. ERIN E. McDONALD, GAIL D. JEFFERSON, PhD, ANDREW J. WHELTON, PhD andTINH NGUYEN, PhD 22Significance of MWCNT and TiO2Nanofillers on Low-Density Polyethylene Thermal & Mechanical Properties:Applicability for Pipes Used In Water & Energy Systems

5. AEREL J. RANKIN, SVETLANA V. POROSEVA andROB O. HOVSAPIAN 27Power Curve Data Analysis for Rim Driven Wind Turbine

6.

TEZESWI P. TADEPALLI andP. RAJU MANTENA 33Blast Response of Sandwich Composite Structural Panels

7. RODWARD L HEWLIN, Jr. andJOHN P. KIZITO 39Evaluation of the Effect of Simplified and Patient-Specific Arterial Geometry on Hemodynamic Flow in Stenosed CarotidBifurcation Arteries

8. IBRAHEEM R. MUHAMMADandJOHN P. KIZITO 45Evaluation of Pulse Jet Mixing Using a Scalar Quantity and Shear Stress

9. ABDUL SHAKOOR, ARSHAD MEHMOOD, KHIZAR AZAMand RIAZ AKBER SAYYED 53Manufacturing Knowledge Sharing: The Utilization of Knowledge Base Technologies in PLM

10. ADEEL KHALID 60Multidisciplinary Design Optimization of Aerospace Vehicle: Single Engine Rotorcraft

11. RICHARD OPOKU andJOHN P. KIZITO 68Optimal Liquid Film Thickness of an Evaporating Film Down an Inclined Plane for Maximum Heat Flux in Spray Cooling

Applications

ASME 2011 Early Career Technical Journal - Vol. 10 ix


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12.

YACOB M. ARGAW andJOHN P. KIZITO 73Similarity Solution for Flow Over a Wedge with Phase Change

13.

RAMBOD RAYEGAN andYONG X. TAO 78Solar Power Generation: Methods and Comparisons

14.

WUQAYAN ALWUQAYAN andSABRI TOSUNOGLU 87Fuzzy-Logic Health Monitoring Robots for the Elderly

15.

KWABENA A. KONADU and SUN YI 91Design of a Controllers for a Laser Beam Stabilizer Using PID and Observed-Based State Feedback Control

16.

CAITLIN PLUNKET, AUSTIN YUILL, DAVID BRANSCOMB andCHAD RODEKOHR, PhD 99Development and Testing of a Diamond Braided Eye Splice

17.

REETA WATTAL andSUNIL PANDEY 105Effect of Welding Parameters on Metallurgical Transformations of Aluminium Alloy 7005

18.

JAIME MUDRICH, ANDRES PACHECO, LEONARDO AMPIE andSABRI TOSUNOGLU 113Development of a Modular Companion Robot for the Elderly

19. NASTASSJA DASQUEandFREDERICK FERGUSON 120The Design and Validation of Waveriders Derived from Axisymmetric Flowfields

20. JOSE MATOS andSABRI TOSUNOGLU 126Peristaltic Crawler for Pipeline Unplugging

21.

TRENICKA ROLLE andRAMANA PIDAPARTI 136A Study of Tissue Strains Induced in Airways Due to Mechanical Ventilation

22.

FRANCIS S. FERNANDEZ, BADER ALE, ALFONSO PARRA andSABRI TOSUNOGLU 142Ocean Wave Energy Generator

23.

MANDEEP SHARMA, MATTHEW LOUSTEAU andINGMAR SCHOEGL 149An Experimental Study of Small-Sized Conical Spouted Beds

AUTHOR INDEX 157

ASME 2011 Early Career Technical Journal - Vol. 10 x


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Clearly, it implies that terminal settling velocity is high for

large particles (Cc 1, and , g, are constant for a givenbioaerosol). Therefore mathematically, the GS depositioneffects on larger particles should be higher.

Figure 1 illustrates how the GS deposition becomes

significant for larger (either large aerodynamic diameter or

became large due to coagulation) particles and low inclinedangle () airways of the human lung. It also shows thatgravitational force on particle exceeds the influence of

Brownian diffusion force (FDiffusion), which is dominant for

smaller particles.

So far, direct in-vitro or in-vivo experiments to determine

the GS deposition effects in human respiratory systems arehardly available. Published literatures only include deposition

fractions (out of total inhaled aerosols) in the distal airways and

alveolar duct regions, where the GS and diffusion may occurprimarily. Based on a comprehensive literature survey on lung

deposition mechanisms it is found that the GS deposition in the

tracheobronchial (TB) and distal airways, and the alveolar ducts

are estimated via analytical equations derived from the

sedimentation losses in flows between parallel plates or circulartubes/channels [5-7].

Computational fluid dynamics (CFD) simulations for the

GS deposition started in the early 1990s, focusing on distal

airways and alveolar ducts. One such simulation study for theasymmetric single bifurcating airway representing generations

15 and 16 showed that the GS on a 10 m particle was verymuch dependent on gravity angle for determining particle

distribution and localizes doses [8]. Using a 2-D symmetric six

generation CFD model, Darquenne and Prisk (2003) reported

that gravity is an important deposition mechanism for 0.5 and 1

m particles in the human acinus [9]. Using a 3-D CFD

simulation for the larger (dp 5 m) particle in the bronchial

airways (generations 6-9) Kleinstreuer et al. (2007) reportedthat, though the inertial impaction is still dominant depositionmechanism in these airway regions, the GS may become

significant with certain gravity angles ( 40o). Two other

studies employing 3-D simulation on alveolar ducts

(generations 18-22) concluded that the total deposition of

aerosol particles can be a function of the gravity angle, and theratio of the terminal setting velocity to mean lumen flow

velocity [10,11]. In summary, most of the CFD simulations

reported the dominance of GS while studying distal airways

and alveolar ducts. The effects of GS on the bioaerosoldeposition in the TB airways have not been thoroughly

investigated.

The aim of this study is to understand the GS of evaluatinginhaled bioaerosols in determining the extent to which GSaffects their transport and deposition characteristics in the upper

TB region. In order to examine this issue, the following

objectives were investigated.

1) Simulation of the effects of gravitational force, FGS, on

submicron particles while flowing through the trachea and main

bronchus. Flow conditions were turbulent through laminar

because the Reynolds number of trachea and main bronchi

ranges from 2214 to 1634 for an inhalation rate of 28.3 L/min2) Solving gravitational force equations of fluid dynamics by

employing object-oriented programming code C++. 3)

Calculation of the initial particle profiles, particle deposition

factors, inlet and wall mass flow rates by using MATLAB. 4)Discretization of all transport equations were done to at least

second order accurate in space. 5) Model results were analyzed

and compared with data reported in the literature.

MATERIALS AND METHODSAirway Geometries

As alluded to in the Introduction section, the GS effects on

particles of various sizes were simulated for the TB regionsFigure 2 illustrates the trachea and main bronchus, two

consecutive lung airway generations (generations 0-1 or G0 and

G1).

The geometric dimensions of the TB model are close to

those given by Weibel (1963) for adults with lung volume 3500

mL. The simulation parameters are listed in the following

Table 1.

Table 1.Summary of the simulation parameters of the in-silicotracheobronchial model.

Simulation Parameter TracheaMain

Bronchus

Lung generation (index) 0 1

Number of airway 1 2

Airway diameter, m 0.018 0.0122

Airway length, m 0.12 0.048

Inhalation flow rate, Q, l/min 28.3 14.15

Bioaerosol flow velocity, m/s 1.86 2.02

Reynolds number,Re 2214 1634

Figure 2. Illustrations of the human lungstrachea and main bronchus (not to scale).

Trachea

Main

Bronchus

ASME 2011 Early Career Technical Journal - Vol. 10 2


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Figure 3 shows 3-D views of a representative single

bifurcation TB regions. The inlet conditions of bioaerosol flow

and particle transport at G0 were selected from outlet in the

upstream Larynx.

Transport Equations

The Lagrangian particle transport (LPT) equation has beenwidely used to simulate the effects of various forces on aerosol

particle trajectories in the respiratory airway [12,13]. Others

estimate that a complete LPT to follow all individual particles

and air parcels as they travel through the entire lungs 24generations would require 10

17 floating point operations and

several terabytes of RAM [14]. In order to overcome this

limitation, simplifying assumptions have been made in the

various simulation studies [15-17].

In order to perform the LPT with a manageable number ofcomputational points, several assumptions were made. These

assumptions include (1) the aerosol particles are spherical in

shape, non-reactive, and stable (i.e., nonevaporating or

noncondensing); (2) Particles and air in the aerosol streamtravel at the same velocity since the submicron particles are

airborne within less than 0.001 second, i.e., negligible

relaxation time; (3) Inhaled aerosols are incompressible and

isentropic, i.e., no effects of thermophoretic anddiffusiophoretic forces; and (4) The human lung is a sequenceof branching circular pipes whose diameters are given in the

idealized Weibel (1963) lung morphometry.

The LPT can be expressed in the following general form.

)1()(and tudt

dx

FFFFdt

dum Braebp

=

+++=

In the above equation, u(t) is the particle velocity and mpis

the particle mass. The forces acting on individual particles

include body forces, Fb (e.g., inertia, gravitation) due to the

particles position in the aerosol streamline, surface forces (Fe)

(e.g., electrostatic space and image forces) due to interactionwith the surrounding particles and airway walls, adhesive

forcesFa(e.g., Van der Waals and capillary forces, interceptive

force), and Brownian diffusive forces, FBr due to irregularwiggling (Brownian) motion of aerosol particles and random

variations in the relentless bombardment of air molecules

against the particles.

Gravitational force on a spherical particle in the respiratory

airway can be determined by the following form of Newtonslaw.

)2(6

)( 3gdF

pgp

g

=

Since the particle density, p, is much larger than density o

fluid (air or gas), g, the equation (2) can be used with close

approximation of the gravitational force by assuming g = 0Moreover, the body force,Fb, is combined effects of inertia and

gravitation several considerations need to be addressed. Forexample, when determining the bioaerosol particle deposition

in the lung due to the GS only, Pich (1972) suggested ignoring

impaction or inertial forces. Finlay (2001) proposedconsidering Froude number (Fr) when taking into account the

importance of inertia force vs. gravitational force. Fr =

U/(gD)0.5,where Uis the mean velocity of the aerosolized fluid

flow, andDis the airway tube diameter. This expression clearlyshows that the GS deposition has impacts on overall particle

deposition in addition to the impaction in the TB airways with

largeFr values.

Numerical Methods

In order to solve governing mass and momentum

conservation equations, user-enhanced finite-volume-based

program, the CFD package Fluent 12.1 from Ansys, Inc wasemployed. The mesh topologies of the TB airway model were

determined by refining the meshes until the grid independence

of the flow field and the particle deposition fraction solutions

were achieved. All the computations were performed on an

Intel Core i7-860 8GB DDR3 workstation. The solutions of theflow field were assumed to be converged when dimensionless

mass and momentum residual ratios were


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Figure 5. The gravitational settling deposition ofsubmicron bioaerosol particle in the

tracheobronchial airways. Comparison betweensimulation results and Pich (1972) mathematical

model results.

0

2

4

6

8

10

12

14

16

0.1 0.2 0.4 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0

Pich, 1972

Simulation

0

2

4

6

8

10

12

14

16

0.1 0.2 0.4 0.6 0.8 1.0 2.0 4.0 6.0 8.0 10.0

Pich, 1972

Simulation

DepositionEfficiency,

%

Aerodynamic Diameter, m

Figure 4. Velocity (cm/s) vector profiles ofinhaled bioaerosols in the trachea and main

bronchus regions of the human lung.

where

cos4

3

D

L

U

vsettling=

and being inclination angle measured relative to the

horizontal axis (i.e., = 0ofor horizontal tubular airway).

RESULTS AND DISCUSSIONComputational models on bioaerosol particle deposition

calculate the mean deposition in each lung generation using an

idealized equation for stable laminar airflows. However, the

airflows at bifurcations are very complicated [18], and far frombeing stable and laminar. A diseased lungs geometry and

ventilation differ from a healthy lung. Such differences affect

bioaerosol flow profiles through the lung airway. In order tosimulate the realism, this study adopted an approach of

modeling the affected flow profiles influence on gravitational

settling force on particles which ultimately effect inhaled

bioaerosol deposition.

In this computational model the inhalation cycle was

considered, and the deposition efficiency was defined in asimplified manner as the number of particles deposited in a

region of the lung divided by the number of particles enteringthe region. The model did not account for deposition due to

impaction, interception, Brownian diffusion, and electrostatic

force effects besides the GS as it will make the model run NP-

hard.

Most of the results found in the literature refer to aerosol

consisting of a collection of modispersed particles. In practicethis is hardly the case, and the bioaerosol particles are not only

having an aerodynamic size distributions but also electrostatic

charge distributions. Therefore, this study accountedpolydispersed aerodynamic size and electrostatic charge

distributions of aerosol particles.

Figure 4 depicts the profiles of velocity vector of theparticles inside TB regions at a steady inhalation flow rate o

28.3 L/min. Velocity characteristics are expressed as the

change of colors from blue (12 cm/s) to red (850 cm/s). It can

be seen that particle velocity reaches two log of magnitudefrom inlet point of trachea to near the bifurcation. The increase

of velocity can be explained by the combined effects of

impaction and gravity force. Larger particles tend to deposit in

the bifurcations due to the resulting impacts of both forces.The variation in velocity magnitude in the junction regions

will result in significantly higher residence time of particles in

the main bronchus (see Figure 4). Therefore, the particles in

this region will display more depositing tendency due the GSforce. In addition to the GS, literature reported that the greates

deposition by inertial impaction typically occurs at or near the

first carina, the dividing point or "hot spots" of trachea

bifurcation [4]. Our simulation results also showed thisphenomenon (sudden rise in velocity) for bioaerosols while

approached to the first carina.

The effect of gravitational force magnitude in particles is

presented in Figure 5. The simulated deposition efficiencies dueto the GS in the TB airway as a function of particle size(aerodynamic diameter) and inlet velocity match the correlation

(Eq. 3) very well. Particle deposition efficiency increases with

an increasing size or decreasing velocity because bothparameters not only cause increasing gravity but also longe

residence time. As the particles size increased from 1 micron to

10 micron, gravitational force becomes the more dominant

mechanism. It can be seen that the deposition almost increased

fifteen fold for particularly for larger (dp 10 m) particles

compared to the smaller (dp< 2 m) particles.



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In summary, the good agreements between simulation

findings and theoretical predictions instill confidence that the

present in-silico simulation model is sufficiently accurate to

analyze bioaerosol flow, particle transport and deposition in

human lung airways.In our previous study it was shown that the accounted

charged particles were carrying both positive and negative

charges [19]. Bipolarly charged particles experience attractionamong each other due to Coulombic attraction forces, which

allowed smaller particles to grow larger and therefore tend to

deposit more efficiently due to the GS (because of formation to

a larger mass). The agglomeration due to electromagnetic

forces is consistent with the physical phenomenon ofpersuasion of electromagnetic forces between oppositely

charged particles [12].

A primary implication of this investigation is thatsedimentation or gravitational force on particles may be more

significant in the regional deposition of submicron bioaerosol

particles than previously considered. The Lagrangian particle

transport model directly accounts for inertia and diffusion

forces which may effect the GS deposition. Major limitationsof this study include the assumptions of simplified inlet andoutlet conditions, the use of idealized lung airway geometries,

steady flow, and the symmetric bifurcations. Each of these

factors may affected physical realism of the model predictionsin relation to actual bioaerosol particle deposition reported in

the in-vitro studies.

CONCLUSIONSA flexible lung simulation incorporating gravity effects on

bioaerosol particles has been developed and realized. At this

point, several conclusions can be made based upon the data

generated from this study. Gravitational force on submicron

bioaerosol particles increases exponentially in the order of threewith the increase of their aerodynamic diameter. The GS has 15

times strong deposition effects on a 10 micron particle than that

of a one micron particle. Compared to the Brownian diffusion,

gravitational settling deposition becomes dominant in the lungfor coagulated bipolarly charged particles.

ACKNOWLEDGEMENTThis work was supported by the Mississippi-INBRE

(P20RR016476) funded by the National Center for Research

Resources, National Institutes of Health.

REFERENCES

[1] Weibel, E. R., 1963, Morphometry of the Human Lung,Springer-Verlag, Berlin, Chap. 9.

[2] Ali, M., 2009, Mechanical Tracheobronchial Model for

Human Lung Inhalation Study, ASME Early Career Technical

J., 8(1), pp. 18.1-18.7.[3] Kleinstreuer, C., Zhang, Z., and Kim, C. S., 2007,

"Combined Inertial and Gravitational Deposition of

Microparticles in Small Airways of a Human RespiratorySystem," J. Aerosol Sci., 38, pp. 1047-1061.

[4] Hinds, W. C., 1999, Aerosol Technology: Properties,

Behavior and Measurement of Airborne Particles, Wiley, NewYork, NY, Chap. 11.

[5] Asgharian, B., Price, O., and Oberdorster, G., 2006, "A

Modeling Study of the Effects of Gravity on AirflowDistribution and Particle Deposition in the Lung," Inhalation

Toxicology, 18, pp. 473-481.

[6] Beeckmans, J. M., 1965, "The Deposition of Aerosols in

the Respiratory Tract I. Mathematical Analysis and Comparison

with Experimental Data," Canadian J. Pharmacol., 43, 172-175[7] Pich, J., 1972, "Theory of Gravitational Deposition of

Particles from Laminar Flows in Channels," J. Aerosol Sci., 3,

351-361.[8] Hofmann, W., Balashazy, I., and Koblinger, L., 1995, "The

Effect of Gravity on Particle Deposition Patterns in Bronchial

Airway Bifurcations," J. Aerosol Sci., 37(1), pp. 1161-1168.

[9] Darquenne, C., and Prisk, G. K., 2003, "Effect of

Gravitational Sedimentation on Simulated Aerosol Dispersionin the Human Acinus," J. Aerosol Sci., 34, pp. 405-418.

[10] Haber, S., Yitzhak, D., and Tsuda, A., 2003, "Gravitationa

Deposition in a Rhythmically Expanding and ContractingAlveolus," J. Applied Physiol., 95, 657-671.[11] Harrington, L., Prisk, G. K., and Darquenne, C., 2006,

"Importance of the Bifurcation Zone and Branch Orientation in

Simulated Aerosol Deposition in the Alveolar Zone of the

Human Lung," J. Aerosol Sci., 37(1), pp. 37-62.[12] Finlay, W. H., 2001, The Mechanics of Inhaled

Pharmaceutical Aerosols: An Introduction, Academic Press,

San Diego, CA, Chap. 1.

[13] Xi, J., Longest, W. P., and Martonen, T. B., 2008, Effects

of Laryngeal Jet on Nano- and microparticle Transport andDeposition in an Approximate Model of the Upper

Tracheobronchial Airways, J. Applied Physiol., 104, pp. 1761-

1777.[14] Fuchs, N. A., 1964, The Mechanics of Aerosols,

Pergamon Press, Oxford, UK. Chap. 3.

[15] Longest, P. W., and Xi, J., 2007, Computational

Investigation of Particle Inertia Effects on Submicron Aerosol

Deposition in the Respiratory Tract, J. Aerosol Sci., 38, pp.111-130.

[16] Herranz, L. E., Cabrer, A., and Peyres, V., 2000,

Modeling Intertial Impaction within Pool Scrubbing Codes, JAerosol Sci., 31(1), pp. S43-S44.

[17] Park, S. S., and Wexler, A. S., 2007, Particle Deposition

in the Pulmonary Region of the Human Lung: A Semi-

empirical Model of Single Breath Transport and Deposition, J.

Aerosol Sci., 38, pp. 228-245.[18] Jan D. L., Sharpiro, A. H., and Kamm, R., 1989, SomeFeatures of Oscillatory Flow in Model Bifurcation, J. Appl.

Physiol., 67, pp. 147-159.

[19] Ali, M., 2010, In-silico Simulation of ElectrostaticCharge Effects on Inhaled Aerosol Particle Deposition in the

Human Lung," ASME Early Career Technical J., 9(1), pp. 75-

79.



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ASME Early Career Technical Journa2011 ASME Early Career Technical Conference, ASME ECTC

November 4 5, Atlanta, Georgia USA

DYNAMIC MODELING AND PARAMETER ESTIMATION FOR IMPACT ANALYSISAND ROBUST PACKAGING DESIGN

Thomas Stone, Seung-Kyum Choi, PhDG. W. Woodruff School of Mechanical Engineering

Georgia Institute of TechnologyAtlanta, GA, USA

Hemanth Amarchinta, PhDBecton Dickinson and Company

Franklin Lakes, NJ, 07417

ABSTRACTOrganizations desire cost-effective methods for evaluating

the integrity of packaging designs. Packaging models enable

optimization of packaging designs such that product failure is

minimized during shipping and handling. This paper presents a

practical, cost-effective method for developing mass-spring-

damper (MSD) packaging models and estimating modelparameters for product packaging.

System model development requires the determination of

parameters for complex spring and damper models, which can

be difficult to find via standard measurement techniques. In

addition, these complex elements, along with other system

model features (e.g., wall friction, colliding components, etc.),

introduce nonlinearities into governing equations. Care must be

taken to choose appropriate spring/damper models and model

features, such that experimentation cost is reduced and accurate

impact analysis results are obtained.

For a particular packaging design example, three MSD

models are compared and contrasted with respect to (a) number

of unknown parameters, (b) model response versus

experimental response data, and (c) model response, namely,

contact force between individual packages. All of the models

were capable of simulating the trends in experimental data, and

maximum contact force values were within 10% of the

corresponding average value. The validated MSD system could

be used to optimize the packaging design.

PROBLEM INTRODUCTIONCurrent research investigates methods for modeling

systems by implementing mass-spring-damper (MSD)systems.

Different systems require varying complexity in spring/damper

models and system model features. Systems of interest include

laminates subject to impact [1], automobile/passenger

interaction [2], structures [3], produce subject to shipping andhandling [4], etc. Moreover, different systems require unique

measurement and experimentation techniques in order to find

model parameters. This research is related to the development

of an MSD system for packaging designs subject to shipping

and handling.

Significant cost is saved in shipping and handling by

inserting individually packaged products directly into shipping

cartons, without using impact absorption packaging material

The effect on profit margin is substantial when the cost of the

individual products is low. When individually packaged

products are directly inserted into the carton, the produc

packaging itself acts as the impact absorber for the producduring shipping. However, this packaging design can result in

product failuresparticularly in the case where a compromised

product package results in complete product failure (e.g.

packaged food, sterile medical products, etc.).

This research develops a generalized MSD representation

for a generic packaging design, and then applies the developed

methodology to a particular packaging example. Severa

complexities are handled, such as stiffness and damping

coefficient determination, spring/damper model development

product-packaging interactions, and packaging-carton

interactions, among other features.

PACKAGING MODEL ASSUMPTIONS ANDDEVELOPMENT

In this research, the generic packaged design consists of

individually packaged products inserted into a standard

shipping carton, (slight modifications can be made to accoun

for different carton shapes). Figure 1ashows a 3D lattice

representation of the individually packaged products, and

Figure 1b shows the corresponding 2D lattice of the products

and the carton. In this particular packaging design, there are 8

rows of product packages and 12 columns of product packages.

Each product package is treated as an individual mass, and

the interaction between the masses is modeled using spring and

damper modelsseeFigure 1c. In this initial model, each mass

has 2 degrees of freedom: horizontal and vertical. The model is

further simplified by considering only flat drops, where thepackaging is not dropped on an edge of the carton or a corner of

the carton. Thus, only one degree of freedom, vertical, must be

considered in the model.Also, when assuming a flat drop, each

row of masses can be consolidated into individual larger

masses. The mass combination is depicted in Figure 1d. The

springs and dampers in parallel can also be combined.



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MULTIPLE COLLISIONMODELINGThe interaction of the MSD components must be modeled

with sufficient intricacy in orderto accurately match actual

system behavior. Therefore, care must be taken to limit the

numberof required modeling parameters such that the final

model can be determined via limited experimentationand low

computational costs.

A critical feature of the system model is the multiple

collisions that occur between the product packages.The product

packaging, which enclosing the actual product,is the first

system component that interacts with any other system

component during impact, (contact phase 1). Then, once the

product packaging is sufficiently deformed, the actual productbegins to interact with other system components, (contact phase

2).

Due to the existence of colliding components, the package

system is modeled as an n-degree-of-freedom oscillator with

elastic stops. The vibro-impact characteristics of an MSD

model with elastic stops were investigated in Ref. [1], including

dynamics and stability during oscillation. The model shown in

Figure 2 is a slight modification of the model used in Ref. [1].

Figure 2. MSD model with elastics stops; both elasticstops for each mass are capable of disengaging

from the adjacent mass

(a) (b)

(c) (d) (e)

Figure 1. (a) 3D lattice representationof individually packaged products (shown as circles), (b) corresponding 2Dlattice composed of products and carton (carton length is into the page), (c) MSD system representation ofpackaged products (shown as masses), (d) MSD system with combined masses, and (e) MSD system with

combined springs and dampers



18/48

In Figure 2, mn is the combined mass of the nth row of

product packages, kpand ksare combined stiffness coefficients,

cpand csare damping coefficients,gis the gap between product

packaging and the enclosed product, qn is the generalized

position coordinate for mass n, andfn is the generalized forcing

for mass n. As briefly mentioned, three modes of packaging

interaction are possible for the model, as shown inFigure 3.

Contact Phase:Contact phase 1 involves the exclusive

interaction of the packaging materials only. The packaging

stiffness and damping coefficients are represented as kpand cp,

respectively.

Contact Phase 2: After the gap, g, decreases to zero, contact

phase 2 is initiated. Contact phase 2 involves the interaction of

the packaging material along with the interaction of the

enclosed products. The product stiffness and damping

coefficients are represented as ksand cs, respectively.

No-contact Phase: If the packaging carton is dropped from a

sufficient height, the packaged products may rebound and lose

contact with each other, resulting in the no-contact phase. A

model of the no-contact phase is not necessary when

considering only the initial impact, but it is useful for parameter

estimation.

Of particular interest is the contact force between the

masses in the system. The contact force on the bottom of a

package,Fc, is

(1)

where p and s are the deformations of the product package

spring and product spring, respectively. and are thedeformation rates product package spring and product spring,

respectively. Positive values of deformation, , correspond to

spring compression. The gap, g, is approximated by acquiring

the distance of product package deflection via experimentation.

Equation (1) describes the interaction of the packages only

during contact phases.When the packages are in the no-contac

phase, the interaction of the package with the carton wall and

gravitational forces dictate the motion of the packagesno

depicted inFigure 2.The low weight of each product package

causes the wall interactions to be the prevalent contributor to

forcing during the no-contact phase. Consequently, the wal

interactions can be approximated by observing the response

during of the packages during the no-contact phase.The

equations of motion during this phase are defined by

(2)where is an empirically determined constant, and qn is the

generalized coordinate of the nthmass, andFwis the wall force

acting on the nthpackage.

Negative deformation, , values correspond to spring

tension. The product package cannot act as a spring in tension

so the dominate forcing is due to interactions between the

product package and the carton wall. Equation (2) assumes tha

the wall interaction force parameters are some proportion, , o

the product package spring and damper parameters. In addition

notice that the wall interaction forces depend on the

displacement and speed relative to the carton wall, and respectively. However, the effects of the no-contact phase are

only needed for the estimation of a few parameters. Thus, a

heuristic estimation approach is suitable.

Equations (1) and (2) are then applied to all of the masses

in order to develop a system of equations that defines the entire

MSD system. The number of equations in the system is equal to

the number of rows of product packages in the particular

packaging design. The equations of motion in general matrix

form is

(3)where M is the inertial matrix, q is the generalized coordinate

vector, Fc,bot is the contact force vector for the bottom of the

masses, Fc,top is the contact force vector for the top of the

masses, Fw is the wall interaction force vector, and f is the

generalized force vector.

The existence of the elastic stop results in piecewise

ordinary differential equations. Moreover, the local solution of

an equation of motion for mass ndepends on the local solution

of the equation of motions for mass n+1 and mass n-1. Thus

traditional transient solution equations are not applicable

MATLAB is used to simultaneously solve all of the equationsof motion while considering elastic stops and initial conditions.

PROPOSED SPRING/DAMPER MODELSIn this section, various spring and damper models are

explored for use in the model shown in Figure 2. The spring

and damper must be modeled with sufficient intricacy such that

results are accurate; however, care must be taken to limit the

complexity such that the final model is useful. Equations (1

(a) (b)

Figure 3.Depiction of (a) contact phase 1 and (b)contact phase 2 for packaged product with arbitraryshape



19/48

and (2) employthe Kelvin-Voigt model [5] for viscoelastic

materials. This is a simple model that results in piecewise linear

differential equations; computations of this model are relatively

straightforward. The softer spring and damper elements that

represent the phase 1 loading, kpand cp, can be assumed to be

linear elements.

However, the Kelvin-Voigt model may not adequately

describe the response of the spring and damper elements that

represent phase 2 loading,i.e., the phase 2 loading spring force

may not be linearly related to spring deformation, and the

associated damping force may not be linearly related to the

spring deformation rate. Preliminary inspection of packaging

design shows that these nonlinearities do exist. Choosing the

appropriate models will limit the complexity of the spring and

damper models and reduce the experimentation cost, while also

delivering sufficiently accurate results.

For the purpose of discussion, we will consider the partial

contact force, Fc, associated with the product spring and

damper elements, ksand cs. The total contact force, Fc, includes

the contact force contributed by both the soft and hard spring

and damper elements.

Nonlinear Viscoelastic Models

Hunt and Crossley presented one of the first nonlinear

viscoelastic contact force models[6].

(4)where is the deformation of the product packaging spring,and is the deformation rate of the product packagingspring. A similar formulation of Eq. (4) was found in Ref. [7].

The kpvalue can easily be determined by static experimentation

setups. However, researchers investigating Eq.(4) identify the

damping coefficient, cp, as a function of a coefficient of

restitution, elastic moduli, and/or Poisson ratiosassuming

spherical contact. For example, the damping coefficient can be

defined as [6]

(5)where e is the coefficient of restitution, and vi is the initial

velocity. The coefficient of restitution is often difficult to

determine for product packaging, and contact between product

packages is often not characterized as spherical contact. Also,

elastic moduli and Poisson ratios are not easily acquired for

product packaging.

Based on empirical observations, another nonlinear

viscoelastic model was introduced in Ref. [8]:

(6)The advantage of this model for this research is that the

damping coefficient is also determined by an empirical

relationship. The formulation of the damping parameter is

(7)where is an empirically determined constant, and m is theeffective mass.

Generalized Exponential ModelA slightly modified version of the contact force

formulation [9]is

(8)where a and b are empirically determined parameters. This

model provides freedom in determining an appropriate contac

force model for contacts involving non-spherical elements. In

the standard Hertz model, a is equal to 3/2, as seen in the

aforementioned models. However, non-spherical contact may

require different values of a.

GeneralizedPolynomialModelsThe vibration dynamics of a system were analyzed in Ref

[10], while considering the following spring and damper

contact force models:

(9) (10)where Fc,spring and F

c,damper are the spring contac

force,respectively. There are multiple stiffness coefficientsks,and ks,2and multiple damping coefficientscs,1and cs,2.

The formulations in Eqs. (9) and (10) can be generalized

according to

(11) (12)

where ks,iis the ithspring coefficient, and cs,jis the j

thdamping

coefficient. Note that the units of the ks and cs terms change

according to the values of i and j, respectively. Thismode

provides a great amount of freedom in empirically matching the

actual system. But the number of unknown parameters can be

as large as i+j.

Proportional Damping

A popular method of approximating the damping

coefficients is assuming that it is proportionally related to thestiffness and mass parameters. Equation (13) describes the

relationship between stiffness, mass, and damping matrices

when proportional damping is assumed.

(13)

where and are empirically determined constants. For the

case where dampers are parallel to spring elements, rather than



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being connected to ground, the damping coefficients are

exclusively proportional to the stiffness coefficients:

(14)

This relationship is useful when there are multiple, unique

spring and damper coefficients. In this case, only the spring

coefficients and one empirical constant, , must be determined

via experimentation.

This research compares and contrasts three spring/damper

models for the contact phase 2 elements: the Kelvin-Voigt

model, Eq. (6), and Eqs. (9) and (10). Contact phase 1 is

modeled using Kelvin-Voigt model for all three system models.

Table 1 shows the contact force expressions for each system

model.

EXPERIMENTAL METHODS AND PARAMETERESTIMATIONContact Phase 1 Experimentation

When a carton of product packages is dropped from a low

height (approximately 1 to 2 cm), no collisions occur between

the enclosed products; i.e., the kp and cp parameters have no

influence on the MSD model. Thus, drop tests from a lowheight are used to determine the kp and cs parameters for a

particular packaging design. In the experimental setup, a laser

doppler vibrometer (LDV) is focused on a reflective tape that is

applied to a product package.The LDV records the velocity of

an individual product on the n-1 row as the carton is dropped

from a low height.

Figure 4 and Figure 5 present graphs of the velocity

response of the n-1 row of product packages. Figure 4 shows

theoretical MSD responses for various kp values. The

appropriate kpvalue is chosen by matching the theoretical MSD

response to the experimental velocity response data. Notice that

increasing the packaging stiffness coefficient alters the

magnitude and frequency of the system response. Figure 5presents the same experimental data along with MSD responses

for various cp values. The cp value is also chosen by

comparisons to experimental data. Notice that the changing

stiffness parameter primarily affects the magnitude of the

response, and also has an effect on the response frequency. It

can also be noted, some noise has been removed from the

experimental data at around 0.05 sec.

Contact Phase 2 Experimentation

When a carton of product packages is dropped a large

distance, the contact phase 2 parameters are requiredks and csThese parameters correspond to the collision of the products

which calls for the inclusion of elastic stops in the MSD

model.The experimental setup using the LDV is not sufficien

to record data for high drop tests. Thus, accelerometers are

attached to individual packaged products within the carton. The

row of the measured packaged product is recorded.

Figure 6 presents the experimental response of measured

rows of product packages, along with responses calculated

using the three MSD system models. Of particular interest isthe maximum acceleration of the package, and the impulse

time. Variations in the accelerometer reading after impact

(0.004 sec to 0.014 sec) are caused by the vibration of the

accelerometer components, and do not reflect the actua

response of the packaging. Contact phase 2 model parameters

are modified such that the theoretical MSD response matches

the experimental acceleration data. The data in Figure 6

supports the claim that each of the contact force models can be

Figure 5. Velocity response of packaging subject to

0.5 in drop; data shown for n-1 row;ksis held constantat 28,000. N/m

Figure 4. Velocity response of packaging subject to0.5 in drop; data shown for n-1 row; csis held

constant at 84.0 N-s/mTable 1. Contact force models

Contact Phase 2 ModelNumber ofUnknowns

Model 1 2Model 2 2Model 3

4



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Figure 6. Acceleration response of MSD system subject to 30 in drop; data for 7th

product package row shown

used to accurately model the actual system. However, the data

also shows that contact phase 2 may need to be divided into a

loading phase and an unloading phase. The model and

theoretical data exhibit different trends in the unloading phase.Fortunately, the loading phase and maximum acceleration are

the key features when considering impact analysis.

The estimated model parameters are presented in Table 2.

The same contact phase 1 model was used for all three system

models. Thus, the associate parameter values are the same

throughout. Model 3 has four unknown parameters: ks,1, ks,2,

cs,1, and cs,2.

CONTACT FORCE RESULTSThe composition of the contact forcespring force,

damping force, and total forceexperienced by the bottom of

an individual package on row two is shown in Figure 7. The

damping force is the primary contributor to the maximum

contact force. Model 1 is used to produce this data; other

models show the same trend.

The contact force for individual product packages onvarious rows is shown inFigure 8.Notice that the packages on

the bottom row experience a significantly higher contact force

than the other products. Thus, it is important to understand the

interaction of the packaging carton and product packages. Even

though the contact force is highest on the bottom, an increased

surface area may contribute to lower rate of failure for

packages on the bottom row.

Figure 7. Theoretical contact force responsecomponents for Model 1; data for 2

ndproduct

package row shown

Table 2. Parameters estimations

kp(N/m)

cp(N-s/m)

ks cs

Model 1 28000. 84 1130. 81

Model 2 28000. 84 35000. 2835

Model 3 28000. 84 1400. 1260.

1.12x109

1.12x108



22/48

The results of the 3 proposed models are presented in

Figure 9.The transient contact force experienced by the bottom

of a product package on the second row is shown. All three

models show similar trends, and the maximum contact force for

the second row does not vary from the corresponding average

contact force by more than 10%.Table 3 includes data for the

second row of packages, as well as the bottom row of packages.

The bottom row of packages experiences a significantly higher

contact force, and shows more variation between models.

However, further investigation is required in order to interpret

bottom row contact force. As aforementioned, an increased

surface area may contribute to lower rate of failure for

packages on the bottom row.

CONCLUSIONS AND FUTURE WORKPreliminary results show that all three models produce

similar results. Variation in the maximum contact force for the

second row is less that 10%, relative to the average value. The

bottom row contact force values have more variationup to

18.5%; the interaction of the bottom row of syringes with the

carton may require a more complex spring/damper model

Since all three system models exhibit comparable performance

the simple Kelvin-Voigt modelModel 1should be chosen inorder to minimize the number of model parameters required

while maintaining sufficient model accuracy, for this particular

packaging design. The increased number of unknowns in

Model 3 did not significantly improve the ability to match the

system model to experimental data. For future testing, pressure

sensors can be used to measure contact pressure values

whereby, contact force measurements can be derived. The

significance of the model parameters will be determined. The

implemented MSD model can be integrated intoa conventiona

design process, such as the reliability-based design

optimization.

REFERENCES[1] Yang, M., and Qiao, P., 2005, "Nonlinear impact analysis o

fully backed composite sandwich structures," Composites

Science and Technology, 65(3-4), pp. 551-562.

[2] Liang, C.-C., and Chiang, C.-F., 2006, "A study on

biodynamic models of seated human subjects exposed to

vertical vibration," International Journal of Industria

Ergonomics, 36(10), pp. 869-890.

[3] Muthukumar, S., and DesRoches, R., 2006, "A Hertz

contact model with non-linear damping for pounding

simulation," Earthquake Engineering & Structural Dynamics

35(7), pp. 811-828.

[4] Van Zeebroeck, M., Lombaert, G., Dintwa, E., Ramon, H.

Degrande, G., and Tijskens, E., 2008, "The simulation of the

impact damage to fruit during the passage of a truck over aspeed bump by means of the discrete element method,"

Biosystems Engineering, 101(1), pp. 58-68.

[5] Carcione, J. M., Poletto, F., and Gei, D., 2004, "3-D wave

simulation in anelastic media using the KelvinVoig

constitutive equation," Journal of Computational Physics

196(1), pp. 282-297.

Table 3. Theoretical maximum contact force values

Max Contact Force,Fc(N)

Variation of Max Fcfrom Average Fc(%)

BottomRow

SecondRow

BottomRow

SecondRow

Model 1 55.6 29.0 -10.8 0.928

Model 2 57.5 26.0 -7.71 -9.51

Model 3 73.9 31.2 18.5 8.58

Figure 9. Theoretical contact force response for threemodels; data for 2

ndproduct package row shown;

contact force is experienced on bottom side ofpackages

Figure 8. Theoretical contact force response forModel 1; data for first four product package rows

shown; contact force is experienced on bottom sideof packages



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[6] Hunt, K. H., and Crossley, F. R. E., 1975, "Coefficient of

Restitution Interpreted as Damping in Vibroimpact," Journal of

Applied Mechanics, 42(2), pp. 440-445.

[7] Kuwabara, G., and Kono, K., 1987, "Restitution Coefficient

in a Collision between Two Spheres," Japanese Journal of

Applied Physics, 26, p. 1230.

[8] Tsuji, Y., Tanaka, T., and Ishida, T., 1992, "Lagrangian

numerical simulation of plug flow of cohesionless particles in a

horizontal pipe," Powder Technology, 71(3), pp. 239-250.

[9] Cochran, A. J., 2002, "Development and use of one-

dimensional models of a golf ball," Journal of Sports Sciences,

pp. 635-641.

[10] Zhu, S. J., Zheng, Y. F., and Fu, Y. M., 2004, "Analysis of

non-linear dynamics of a two-degree-of-freedom vibration

system with non-linear damping and non-linear spring," Journal

of Sound and Vibration, 271(1-2), pp. 15-24.



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ASME Early Career Technical Journa2011 ASME Early Career Technical Conference, ASME ECTC

November 4 5, Atlanta, Georgia USA

MOTION TRACKING THROUGH MACHINE VISION

Poonam Savsani andVimal Savsani

B. H. Gardi College of Engineering and TechnologyRajkot, Gujarat, INDIA

ABSTRACT

The advance of technology makes video acquisition

devices better and less costly; thereby, increasing the number of

applications that can effectively utilize digital video. Compared

to still images, video sequences provide more information

about how objects and scenarios change over time. This work

deals with the tracking and following of a single object in asequence of frames and the co-ordinate of the object is

determined. The object tracking video is recorded with the help

of web cam and then recorded video is converted into AVI

format with 20 fps using Roborealm software. This AVI format

video is processed in developed Matlab code for tracking the

object. The object is tracked by plotting a rectangular bounding

box around it in each frame. Co-ordinate of the centroid of that

bounding box is determined for the further evaluation. An

algorithm is developed for improving the image quality,

segmentation and feature extraction. Segmentation is

performed to detect the object after reducing the noise from that

scene.

1. INTRODUCTION

It has always been a dream to have a robot with the same

abilities as a human being. Ten years ago, people could only

imagine the things robots are capable of today, but the road to

an autonomous robot that can act as a human, is still very long.

The fact that these robots have to be autonomous means that

they have to do everything without interference of humans. One

of the keys to success is a good vision system, so the question

is: how to make a robot see like a human? The only way for

now to approach human sight is with the use of a camera, so

that is what will be further developed in the future.

The main objective of the present work is to develop theobject tracking algorithm that will provide a communication

path to hardware system. For this purpose a matlab code is

developed which takes AVI video. Video is recorded by using

web camera and it is formatted into the required format by

using Roborealm software. The whole object tracking algorithm

is divided in two parts:

1. Roborealm software

a) Removing noise from the imageb) Extracting green color from the imagec) Recording the video in AVI format

2. Matlab codea) Segmentation of the recorded videob) Feature extractionc) Tracking the moving object

1.1 Related work

Cohen and Medioni [2] address the fragmentation

problem by requiring temporal coherence of blobs. They do no

handle groups of objects. Gabriel et al [3] review other multi

object tracking methods. There has been some work on tracking

groups of objects [1]. Gennari and Hager [4] propose a group-

tracking algorithm where objects and fragments are not

distinguished from groups. Yiwei Wang et al [5] proposes an

algorithm to track moving objects in video sequence. The

algorithm first separates the moving objects from the

background in each frame. Then, four sets of variables are

computed based on the positions, the sizes, the grayscale

distributions and the presence of textures of the objects. A rule-based method is developed to track the objects between frames

based on the values of the variables. S. Sulaima et al [6

proposed system employed background modeling and image

differential techniques to isolate the motion objects from its

background. Additionally, morphology processes are performed

to enhance the object pixels that have been extracted so that a

more accurate output image that comprised of the detected

object can be determined. Bing Leng and Qionghai Dai [7]

address the problem of extracting video objects from head-

shoulder video sequences. A method based on accumulative

frame difference is proposed. Biswajit Bose et al [8] propose a

framework for detecting and tracking multiple interacting

objects, while explicitly handling the dual problems o

fragmentation (an object may be broken into several blobs) and

grouping (multiple objects may appear as a single blob). They

use foreground blobs obtained by background subtraction from

a stationary camera as measurements.

2. OBJECT TRACKING

Object tracking is the process of locating and following

the moving object in sequence of video frames. A web camera



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is used as input sensors to record the video. The recorded video

may have some noise. For tracking the object pre-processing,

segmentation, feature extraction and object detection is

necessary.

2.1 Pre-processing

The pre-processing performs some steps to improve the

image quality. Several methods are explained below.

2.1.1 Mean Filter

Mean filtering is a simple, intuitive and easy to

implement method of smoothing images, i.e., reducing the

amount of intensity variation between one pixel and the next. It

is often used to reduce noise in images. The idea of mean

filtering is simply to replace each pixel value in an image with

the mean (`average') value of its neighbors, including itself.

This has the effect of eliminating pixel values which are

unrepresentative of their surroundings. For every pixel the

mean from the box elements are calculated and the pixel values

are stored in to the central element. Let Sxy represents the set

of coordinates in a rectangular subimage window of size mn,

centred at a point (x,y). the arithmetic mean filter computes the

average value of the corrupted image g(x,y) in area defined by

Sxy. In other words,

f(x, y) =1

mn g(s, t)(s,t)Sxy

This operation can be implemented using spatial filter

of size mn in which all coefficients have value 1/mn. Let us

consider an example with a 3x3 matrix,

1 2 94 3 85 6 7

Pixel value= 1/9(1+2+3+4+5+6+7+8+9)

in the above matrix, central element is '3' and after calculating

the pixel value, the value of 3 is replaced by the pixel value.

The calculation time for mean filter is very less compared to all

other. By using this filter smoothing is done. [9] [10] the two

main problems with mean filtering, which are:

A single pixel with a very unrepresentative value cansignificantly affect the mean value of all the pixels in

its neighborhood.

When the filter neighborhood straddles an edge, thefilter will interpolate new values for pixels on the edge

and so will blur that edge. This may be a problem if

sharp edges are required in the output.

Both of these problems are tackled by the median filter, which

is often a better filter for reducing noise than the mean filter

but it takes longer to compute.

2.1.2 Gaussian Smoothing

The Gaussian smoothing operator is used to `blurimages and remove detail and noise. In this sense it is similar to

the mean filter, but it uses a different function that represents

the shape of a Gaussian (`bell-shaped') hump. A box is scanned

over the whole image and the pixel value calculated from the

standard deviation of Gaussian is stored in the central element

The size of the box may vary from 1 to 7 means 1x1 to 7x7

elements [11].

(,) = 122+2

Where x and y denotes the positions of elements in the

box. The effect of Gaussian smoothing is to blur an image, in asimilar fashion to the mean filter. The degree of smoothing is

determined by the standard deviation of the Gaussian. The

Gaussian outputs a `weighted average' of each pixel's

neighborhood, with the average weighted more towards the

value of the central pixels. This is in contrast to the mean filter's

uniformly weighted average. Because of this, a Gaussian

provides gentler smoothing and preserves edges better than a

similarly sized mean filter. One of the principle justifications

for using the Gaussian as a smoothing filter is due to its

frequency response. Most convolution-based smoothing filters

act as low pass filters. This means that their effect is to remove

high spatial frequency components from an image.

2.1.3 Median Filter

The median filter is a classical noise removal filter

Noise is removed by calculating the median from all its box

elements and stores the value to the central element. The values

of the median of the intensity levels in the neighborhood of that

pixel:

f(x, y) = median(s,t)Sxyg{(s, t)}

The value of the pixel at (x,y) is included in the

computation of the median. If we consider an example of 3x3

matrix

1 2 94 3 85 6 7



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The median filter sorts the elements in a given matrix and

median value is assigned to the central pixel. Sorted elements 1,

2, 3, 4, 5, 6, 7, 8, 9 and median 5 will assign to the central

element. Similar box scan is performed over the whole image

and reduces noise. Execution time is more compared to mean

filter, since the algorithm involves with sorting techniques [12].

Instead of simply replacing the pixel value with the mean of

neighboring pixel values, it replaces it with the median of those

values. The median filter has two main advantages over the

mean filter:

The median is a more robust average than the meanand so a single very unrepresentative pixel in a

neighborhood will not affect the median value

significantly.

Since the median value must actually be the value ofone of the pixels in the neighborhood, the median filter

does not create new unrealistic pixel values when the

filter straddles an edge. For this reason the median

filter is much better at preserving sharp edges than the

mean filter.

2.1.4 Segmentation

Segmentation is the process of dividing digital image

into multiple regions. Segmentation shows the objects and

boundaries in an image. Each Pixel in the region has some

similar characteristics like color, intensity, etc. The purpose of

image segmentation is to partition an image into meaningful

regions with respect to a particular application. Few methods

for segmenting the images are explained below.

2.1.5 Histogram Based Segmentation

Histograms are the basis for numerous spatial domain

processing techniques. One of the simple ways of doing

segmentation is using histogram. Histogram modeling

techniques provide a sophisticated method for modifying the

dynamic range and contrast of an image by altering that image

such that its intensity histogram has a desired shape. Histogram

equalization employs a monotonic, non-linear mapping which

re-assigns the intensity values of pixels in the input image such

that the output image contains a uniform distribution of

intensities. This technique is used in image segmentation

processes. Histogram is computed for all the image pixels. The

peaks in histogram are produced by the intensity values that areproduced after applying the threshold and clustering. The pixel

value is used to locate the regions in the image. Based on

histogram values and threshold we can classify the low

intensity values as object and the high values are background

image (most of the cases)[13]. In figure 1 (a) shows source

image while (b) shows the segmented image after application of

histogram. Assume for a moment that intensity levels are

continuous quantities normalized to the range [0, 1], and let

denote the probability density function Pr(r), (PDF) of the

intensity levels in a given image, where the subscript is used for

differentiating between the PDFs of the input and output

images.

.

(a) Without applying histogram (b) with equalize histogram

Figure 1. Effect of histogram

Suppose that we perform the following transformation on the

input levels to obtain output (processed) intensity levels, s,

=() = ()0

where w is a dummy variable of integration. It can be shown

(Gonzalez and Woods [2002]) that the probability density

function of the output levels is uniform; that is,

() = {10 10

In other words, the preceding transformation generates

images whose intensity levels is equally likely, and, in addition

cover the entire range [0, 1]. The net result of this intensity-

level equalization process is an image with increased dynamic

range, which will tend to have higher contrast.

2.1.6 Frame Difference

Frame difference calculates the difference between 2

frames at every pixel position and store the absolute difference

It is used to visualize the moving objects in a sequence of

frames. It takes very less memory for performing the

calculation.[14]. The frame method basically employs the

image subtraction operator. The image subtraction operator

takes two images as input and produces as output a third image

whose pixel values are simply those of the first image minus

the corresponding pixel values from the second image. The

subtraction of two images is performed straightforwardly in a

single pass. The output pixel values are given by:

Q(i, j) = P1(i, j) P2(i, j)

Let us consider an example, if we take a sequence of frames

the present frame and the next frame are taken into

consideration at every calculation and the frames are shifted

(after calculation the next frame becomes present frame and the

frame that comes in sequence becomes next frame). Figure 2

shows the frame difference between 2 frames.



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Figure 2. Frame difference between two frames

2.1.7 Feature Extraction

Feature Extraction plays a vital role to detect the moving

objects in sequence of frames. Every object has a specific

feature like color or shape. In a sequence of frames, any one of

the feature is used to detect the objects in the frame and that

feature is used to detect the object. Based on color and edge

feature can be extracted,

2.1.8 Edges

Edges are formed where there is a sharp change in the

intensity of images. If there is an object, the pixel positions of

the object boundary are stored and in the next sequence offrames this position is verified. Edge pixels are pixels at which

the intensity of an image changes abruptly and edges are sets of

connected edge pixels. Edge detector is local image processing

methods designed to detect edge pixels. Corner based algorithm

uses the pixel position of edges for defining and tracking of

objects. [15]. Over view of the mathematics behind the edge

detection is given below. The tool of choice for finding edge

strength and direction at location (x,y) of an image, f is the

gradient and defined as follows:

= =

The magnitude of vector is:

=() =2 +212= [(/)2 + (/)2]1/2

Is the value of the rate of change in the direction of the gradient

vactor. Here Gx, Gy and vector are images of the same size as

the original, created when x and y are allowed to vary along all

pixel location in f. the direction of the gradient vector is given

by the angle,

(,) = tan1 Measured with respect to the x-axis.

If the segmentation is performed using frame difference

the residual image is visualized with rectangular bounding box

with the dimensions of the object produced from residua

image. For a given image, a scan is performed where the

intensity value of the image is more than limit (depends on the

assigned value, for accurate assign maximum). In this Features

is extracted by color and here the intensity value describes the

color. The pixel values from the first hit of the intensity values

from top, bottom, left and right are stored. By using this

dimension values a rectangular bounding box is plotted within

the limits of the values produced.

2.1.9 Object Detection

Extraction of objects using the features is known as

object detection. Every object has a specific feature based on its

dimensions. Applying feature extraction algorithm, the object in

each frame can be pointed out.

2.1.10 Optical Flow

Optical flow is one way to detect moving objects in a

sequence of frames. Optical flow or optic flow is the pattern o

apparent motion of objects, surfaces, and edges in a visua

scene caused by the relative motion between an observer (an

eye or a camera) and the scene. Optical flow is the distribution

of apparent velocities of movement of brightness patterns in an

image. In this, the vector position of pixels is calculated and

compared in sequence of frames for the pixel position

Typically the motion is represented as vector position of pixels.

2.1.11 Block Matching

Block matching algorithm is a standard technique fordetermining the moving object in video. Blocks are formed in a

region without overlapping on the other region. The block

matching algorithm is a standard technique for encoding

motion in video sequences. It aims at detecting the motion

between two images in a block-wise sense. The blocks are

usually defined by dividing the image frame into non-

overlapping square parts. Each block from the current frame is

matched into a block in the destination frame by shifting the

current block over a predefined neighborhood of pixels in the

destination frame. At each shift, the sum of the distances

between the gray values of the two blocks is computed. The

shift which gives the smallest total distance is considered the

best match. In the ideal case, two matching blocks have theircorresponding pixels exactly equal. This is rarely true because

moving objects change their shape in respect to the observer's

point of view, the light reacted from objects' surface also

changes, and finally in the real world there is always noise

Furthermore, from semantic point view, in scenes containing

motion there are occlusions among the objects, as well as

disappearing of objects and appearing of new ones. Despite the

problems of pixel by pixel correspondence, it is fast to compute

and is used extensively for finding matching regions [16].



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2.1.12 Tracking

The process of locating the moving object in sequence

of frames is known as tracking. Object tracking is a key

computer vision topic, which aims at detecting the position of a

moving object from a video sequence. This tracking can be

performed by using the feature extraction of objects and

detecting the objects in sequence of frames. By using the

position values of object in every frame, the position of the

moving object can be calculated.

3. ALGORITHM

This section explains the sequential approach to track the

moving object and to calculate the centroid of the object.

Following steps are followed to track the object,

1 Pre-processing: to improve the image quality and getimage in required form.

2 Segmentation: to separate multiple regions in image3 Feature Extraction: to analyze the region on image4 Tracking : Analyzing the position

3.1.1 Pre-processing

To improve the image quality and to get the desired feature

in the image pre-processing is performed. So in pre-processing

three sequences are followed,

1 Color extraction2 Noise removal3 Write video to AVI format

All this three steps are evaluated by using Roborealm software.

3.1.2 Color Extraction

The Matlab code is developed to track the green

colored object; hence RGB filter is used to extract the green

color from the image. The RGB Filter uses RGB values to

focus the attention towards the primary RGB colors. Depending

on the color selected this filter will diminish all pixels that are

not of the selected colors.

For example, if green is chosen:

G = ((G-B)+(G-R))

R = 0

B = 0

G is then normalized with respect to the maximum green value.

Based on the above formula it can be seen that white pixels

result in a zero value whereas pure primary colors (G=255

R=0, B=0) G doubles its value. To filter green color by using

Roborealm software color>RGB filter dialog box is executed

and proper intensity and hue are tuned.

(a) source image (b) filtered image

Figure 3. Color extraction with RGB filter

Figure 3 (a) shows the source file and (b) shows the image after

applying RGB filter with intensity 158 and hue 60.

3.1.3 Noise Removal

Noise is removed from the image by using median

filter. The median filter is used to remove noise from an image

by replacing pixels with the middle pixel value selected from a

certain window size. In Roborealm for the application of the

median filter window size need to be selected. This window

size defines the size of the matrix. Depending on the window

size smoothness of the image is obtained.

(a) (b)

Figure 4. Noise removal with median 3 filter

(a) Source image (b) image after filtering with median 3

Figure 4 (a) shows the source image and (b) shows the image

after application of median 3 filter.

3.1.4 Write Video to AVI Format

Matlab takes AVI format video as input hence processed

video need to be written in AVI format. This can be achieved by

using the write AVI function of Roborealm software. The write

AVI function allows you to save the processed video to an AV

video file. This file can then be played back using any AVI

player (with the appropriate decompressor) when needed



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without running RoboRealm. To use the write AVI function the

following terms should be specified:

1 Which image to save2 Specify the file to save the recording to.3 Specify the time to stop recording.4 Specify the frame per seconds.5 Specify the type of compression.6 Quality of recording.7 Command variable/manual start-stop

For the present work, image is saved in Matlab and for the

recording 20 frames per second are chosen as it require less

memory. Video is recorded in an uncompressing mode. To start

and stop the video recording manual button is used instead any

command variable.

3.1.5 Segmentation

Segmentation is the process of analyzing multiple

regions in a frame. Frame difference algorithm is used to

separate the moving objects in frame. Advantage of using frame

difference over the others is less memory usage and two frames

are needed for calculation and gives good results when single

moving object is to be tracked. To perform the segmentation

operation, frame difference algorithm is implemented as it takes

less processing time. Frame difference algorithm performs

separation of two sequential frames and it is explained in detail

above. Algorithm for the segmentation is explained as follow:

1. Read the input images

2. For (present position=initial position: final position)

(a) Difference between the pixels values at present

position of two images is

calculated

(b) Calculate the absolute value

(c) Store the difference in new image at same pixel

position that is at present position

When the algorithm is implemented, the starting address of the

temporary images (both present and previous images) is takenand the image after the frame difference is stored in the

memory allocated for the output image.

3.1.5 Feature Extraction

Every object has a specific feature which is used to

visualize the object and used for tracking. After performing the

segmentation, a rectangular bounding box is plotted with the

dimensions of the object produced in the residual image

Section 2.3.2 explains a clear view on bounding box. Algorithm

for the bounding box is as followed

1. Read the image difference

2. for (present position=initial value: final value) of Y

resolution

3. For (present position=initial value: final value) of X

resolution

(a) Calculate the sharp change in intensity of image

from top and bottom

(b) Store the values in an array

4. Height of the bounding box is = bottom value- top value

5. For (present position=initial value: final value) of X

resolution

6. For (present position=initial value: final value) of Y

resolution

(a) Calculate the sharp change in intensity of image

from left and right

(b) Store the values in an array

7. Width of the bound box = right value - left value8. Using the dimensions, draw boundary to the imageInitial value: the starting position of the pixel in an image. Final

value: the ending position of the pixel in an image.

= 2

= 2

9. Add the height value with the top value and store it in a

variable like mid top

10. Add the width value to the left value and store it in avariable like mid left

11. Assign the max intensity to the pixel at pixel value at (mid

top, mid left)

When the algorithm is implemented for the image

dimensions of the image are produced and a rectangular

boundary is plotted. By using this bounding box dimensions

we plot the centroid of the box and used for tracking of objects

and determining the velocity of object.

3.1.6 Tracking

The science of motion tracking is fascinating becauseof its highly interdisciplinary nature and wide range o

applications. The aim of our object tracker module is to

generate the trajectory of an object over time by locating its

position in every frame of the video. The possible object region

in every frame is obtained by means of object detection, and

then tracking is done corresponds the object across frames. In

tracking, the object approach is represented using the Primitive

Geometric Shape Appearance Model [1], [3] (i.e. the object is

represented as a rectangle). The object is tracked by considering



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the positional information of the moving object as an input

from the object detection. This information is then used to

extract a square image template (whenever required) from the

last acquired frame. The module keeps on searching it in the

frames captured from that point. Whenever found it displays a

red overlaid rectangle over the detected object. As explained

above object tracking follows few steps. Figure-6 illustrated the

flow of the developed object tracking code. Centroid of

2011 Proceedings Vol. 10 Part 1

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