Introduction to Mechanical Engineering Program at IUPUI

1
Introduction to Mechanical Engineering Program at
IUPUI
Presented to Industrial Advisory Board, October
5, 2001

• H.U. Akay
• Professor and Chair
• Department of Mechanical Engineering
• Indiana University-Purdue University Indianapolis

http//www.engr.iupui.edu/me
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Outline

• Introduction to IUPUI
• Degrees and curriculum
• Research programs and facilities
• Sample projects

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IUPUI
Urban campus ofIndiana Universityand Purdue
University
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• IUPUI enrolls 27,000 students and employs 1,400
  faculty and 7,000 staff
• Offers 179 Indiana University and Purdue
  University degrees
• IUPUIs 65 academic buildings are located on 285
  acres west of downtown Indianapolis

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ME Faculty

• 13 Faculty Members (3 Biomedical Engineering
  (BME) faculty with joint appointments in
  dentistry and medicine
• 2 Emeriti Faculty
• 2 Associate Faculty
• 2 to be hired (thermo fluids and mechatronics)

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ME Faculty and Research Areas

• Dare Afolabi, Ph.D., Imperial College, 1982.
  Structural dynamics, structural stability,
  applied mechanics, nonlinear mechanics, control
• Hasan U. Akay, Ph.D., University of Texas at
  Austin, 1974. Computational fluid dynamics,
  computational mechanics, finite element method,
  parallel computing, fatigue and creep modeling,
  electronic package reliability
• Jie Chen, Ph.D., Drexel University, 1989. System
  design and simulation, hybrid electrical vehicle
  simulation, engineering design, kinematics,
  biomechanics, implantology, joint mechanics,
  mechanics of orthodontics, dental restorations

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ME Faculty and Research Areas (contd)

• Akin Ecer, Ph.D., University of Notre Dame, 1970.
  Computational fluid dynamics, parallel
  computing, dynamic load balancing, finite element
  method
• Hazim El-Mounayri, Ph.D., Mc Master University,
  1997. Advanced manufacturing, intelligent
  machining, CAD/CAM, solid modeling, machining
  process control, simulation and optimization,
  automation
• Andrew T. Hsu, Ph.D., Georgia Institute of
  Technology, 1986. Computational fluid mechanics,
  combustion, reactive flows, turbulence and
  transition modeling, biomedical fluid mechanics
• M. Razi Nalim, Ph.D., Cornell University, 1994.
  Unsteady fluid mechanics, combustion, wave
  rotors, pollution control in combustion engines,
  propulsion

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ME Faculty and Research Areas (contd)

• Peter Orono, Ph.D., Wayne State University, 1991.
  Dynamics, vibrations, and controls
• Nasser H. Paydar, Ph.D., Syracuse University,
  1985. Computational mechanics, biomechanics,
  electronic package reliability, finite element
  method
• Ramana M. Pidaparti, Ph.D., Purdue University,
  1989. Composites, computational intelligence
  applications, biomechanics and biomaterials,
  biomedical engineering device design, fatigue and
  fracture, smart materials and structures,
  fracture mechanics, finite element method

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BME Faculty and Research Areas

• Thomas R. Katona, Ph.D., University of
  Pennsylvania, 1981. Biomechanics, bone fatigue,
  implants, tooth movement, dental restorations,
  orthodontic bracket strength
• Charles Turner, Ph.D., Tulane University, 1987.
  Solid mechanics, biomechanics, biomaterials, bone
  biology, musculoskeletal biomechanics
• Hiroki Yokota, Ph.D., University of Tokyo, 1983,
  Ph.D., Indiana University, 1993. Molecular
  bioengineering, biomechanics, biotechnology,
  bioinformatics, human genomics

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Undergraduate Program

• 150 students (50 in Freshmen Engineering
  Program)
• Over 700 Alumni
• Bachelor of Science in Mechanical Engineering
  (BSME)
• Bachelor of Science in Engineering (BSE)
• Biomechanics
• Engineering management
• Mechatronics
• A Dual Degree Program with Butler (BSME)

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Undergraduate Curriculum

• Physical sciences
• Mathematics
• Computer languages and tools
• Solid mechanics
• Fluid mechanics
• Thermal sciences
• Dynamics, controls, measurements, and
  electro-mechanical systems
• Design and manufacturing
• Computer simulations (CAD/CAE)
• Technical communication
• Professional ethics
• General education (humanities/social sciences)

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Teaching Laboratories

• The following laboratories are maintained in the
  department for undergraduate teaching
• Heat and Mass Transfer Laboratory
• Fluid Mechanics Laboratory
• Design Laboratory
• Mechanics of Materials Laboratory
• Dynamics and Measurements Laboratory

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Graduate Program

• 50 students (thesis and non-thesis options)
• Master of Science in Mechanical Engineering
  (MSME)
• Master of Science in Engineering (MSE)
• Master of Science (MS)
• Ph.D. in collaboration with Purdue WL

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Specialty Areas in Graduate Program

• Solid Mechanics
• Includes advanced manufacturing, advanced
  materials, fracture mechanics, CAE/CAD/CAM,
  computational solid mechanics, vibrations, etc.)
• Fluid and Thermal Sciences
• Includes combustion, computational fluid
  dynamics, finite elements, nanotechnology, etc.
• Biomechanics
• Includes bone and tissue mechanics, computational
  biomechanics, dental mechanics, genomics, etc.

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Masters Degree

• Thesis Option 30 credit hours (seven graduate
  courses thesis)
• Non-Thesis Option 30 credit hours
• Up to six credit hours (two courses) may be
  independent projects recommended for part time
  students

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Graduate Coop Program

• Designed for graduate students to
• Work in industry during alternating semesters
  with salary paid by the sponsoring company
• Involved in advanced/applied projects while
  resident in company under co-supervision of a
  senior company engineer and a faculty member

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Certificate in Computer-Aided Mechanical
Engineering - New

• Designed to address industry's increased needs
  for engineers who can model complex engineering
  design and analysis problems competently using
  computers
• 12 credit hours of course work four courses (as
  opposed to 30 credit hours for Masters)
• Specialty areas
• Computations of Mechanical Systems
• Computations of Fluid and Thermal Systems

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Note 2000/2001 credit hours do not include 400
freshman credit hours
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Note Drop in 2000/2001 graduates is mostly due
to ending of Malaysian 22 program
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Research Focus in the Department

• Three main research areas in the department are
• Advanced Design and Manufacturing
• Biomechanics
• Computational Engineering

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Research Laboratories

• The department maintains the following labs for
  research and graduate education
• Advanced Computer-Aided Engineering and
  Manufacturing Laboratory (ACAEML)
• Advanced Materials Laboratory (AML)
• Biomolecular Engineering Laboratory (BEL)
• Computational Fluid Dynamics Laboratory (CFDL)
• Computational Mechanics Laboratory (CML)
• Experimental Mechanics Laboratory (EML)

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Recent Sponsors/Collaborators

• National Science Foundation (NSF)
• National Institute of Health (NIH)
• NASA
• AFOSR
• Army and Navy
• DARPA
• Naval Surface Warfare Center (NSWC) - Crane
• Indiana 21st Century Science and Technology Fund
• Numerous Indiana companies

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Recent Sponsors/Collaborators

• Raytheon
• Dresser Clark
• Cummins
• Bishop Steering Technology
• Ryobi Diecasting
• Align Technology
• Overton and Sons for Dies and Molds

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Recent Sponsors/Collaborators

• Allison Advanced Development Company (ADDC)
• Allison Transmission
• Carrier Corporation
• Delphi Delco/Delco Remy
• AYT Corporation
• Rolls-Royce Corporation
• TRW
• Eli Lilly

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(No Transcript)
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Recent Research Projects

• Dynamic Load Balancing for Parallel Computing,
  NASA Glenn Research Center
• A New Computational Method for Massively
  Parallel Computational Fluid Dynamics, NASA
  Glenn Research Center
• Parallelization and Development of Solid-Fluid
  Interaction Models for Aeroelasticity
• Benchmarking of Aerodynamic Panel Methods,
  AFOSR
• Development of CE/SE Method for Combustion
  Simulations, AYT Corporation

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Recent Research Projects (contd)

• Advanced Propulsion and Power Institute
  Innovative Propulsion Systems and High-Fidelity
  Computer Simulation, Indiana 21st Century
  Research and Technology Fund
• Deterministic Stress Modeling for Turbulent
  Mixing in Jet-in-Crossflow, Pratt and Whitney
• Steady/Unsteady Chemically Reacting Flow
  Simulation, AYT Corporation
• Pulse Detonation Engine Model Subroutine,
  Allison Advanced Development Company

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Recent Research Projects (contd)

• Wave Fan and Hybrid Pulse Detonation, NASA
  Glenn Research Center
• Two-Stroke Gas Engine Simulation, Dresser-Rand
  Corporation
• Mechanical Effect of Induced Changes in
  Extracellular Matrix of Viceral Smooth Muscle
  Tissues, NSF
• 3D Surface Corrosion Growth Model for Materials
  Design, NSF
• Fatigue Life Prediction Methods for Thermal
  Fatigue of Solder Joints of Electronic Packages,
  Boeing Corporation
•  

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Recent Research Projects (contd)

• Implementation of CAM Post-Processors for
  Raytheon NC Production Machines, Raytheon
  Technical Services
• CAD/CAE/CAM/PDM Integration
• Power Train Simulation for Hybrid Vehicles
• Intelligent Machining
• Geometric Dimensioning and Tolerancing, Bishop
  Steering Technology
• Using Family of Parts for Die Design
  Automation, Overton and Sons for Dies and Molds

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Computational Fluid Dynamics (CFD) Ecer, Akay,
and Chien

• Parallel computing on distributed systems
  workstations and PCs
• Dynamic load balancing for parallel computing
• Aerodynamics simulations
• Turbomachinery flows

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Parallel Computing for Large Scale Industrial
Problems

• Partition the computational domain into smaller
  parts
• Solve each part on network of computers
• Used for large-scale computing

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Parallel Adaptive Flow Solver PACER3D
Grid partitions and adapted grid
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Parallel Adaptive Flow Solver PACER3D
Wing grid and adapted solution
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Parallel Performance
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CFD Laboratory Parallel Computing Environment
INTERNET
Ohio
Indiana
IU Bloomington-IN
NASA/Glenn Cleveland-OH
IUPUI Indianapolis-IN

• LAN (Local Area Network)
• CFD Lab Network in Indianapolis
• 6 IBM RS/6000 (Unix)
• 16 Pentium-II/400 (NT)

• WAN (Wide Area Network)
• Indianapolis and Bloomington
• 6 IBM RS/6000 (Unix) in Indianapolis
• 20 Pentium-II/400 (NT) in Indianapolis
• 139 CPU IBM SP2 (Unix) in Bloomington
• 128 CPU PII/III (Linux) in Cleveland

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Multi-user Parallel Computing Environment
In a multi-user distributed-computing
environment, everybody has different needs.
Whatever their needs, everybody wants priority in
running their programs. In such an environment,
an efficient distribution of ALL those parallel
jobs should be done for benefit of all users.
HUGE MEMORY
HIGH SPEED
Local and Wide Area Computer Networks
JUST RESULTS
Users Available Computers Unavailable
Computers Inaccessible Computers
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Aircraft Engine Simulation with Parallelized
ADPAC (in collaboration with Nasa/Rolls-Royce)
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Dynamic Load Balancing --DLB

• Objective
• Move processes from slow computers to fast
  computers to reduce total computation time
• Method
• Monitor periodically the average communication
  and computation costs of executions on each
  computer during a pre-determined. If needed,
  redistribute the loads by Load Balancer for
  optimum solution time at the end of each cycle

START
Initial block distribution
PTrack, CTrack ADPAC
DLB Cycles
Load Balancer
New block distribution
END
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DLB For A Single Parallel Job Application ADPAC
LAN 100Mb Switch 6 IBM RS/6000 (UNIX)
10 PII (NT4.0)
Initial Block Distribution 4 blocks/processor(equa
l-loading)
Mesh Size 765x25x25 64 Blocks
Extraneous Load
Blocks of Parallel Job
Equal-Sized Blocks (Block Size / Interface Size)
12
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DLB For A Single Parallel Job Application ADPAC
Improvement in 3rd cycle elapsed time 24
LAN 100Mb Switch 6 IBM RS/6000 (UNIX)
10 PII (NT4.0)
Extraneous Load
Blocks of Parallel Job
Mesh Size 765x25x25 64 Blocks
Equal-Sized Blocks (Block Size / Interface Size)
12
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Computational Fluid Dynamics Hsu

• Molecular dynamics and nano-scale flow
  simulations for nano-machines
• Lattice Boltzmann method for multi-scale flow
  simulations
• Combustion simulation and multi-phase flows
• Turbulence modeling

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Simulated Non-dimensional Temperature
Distribution in a Jet-in-Crossflow
Combustion(simulates flows in gas-turbine engine
combustors)
Cross Flow
Jet
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Lattice Boltzmann Method for Turbomachinary Flows
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Computational Fluid Dynamics Nalim

• Modeling of non-steady flow and combustion
  phenomena
• Aerospace propulsion, pulse detonation engines,
  and wave rotor technologies
• Computational fluid mechanics with StarCD

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Combustion Wave Rotor Compact self-cooled design
and reduced pollution. Wave-rotor sketches and
wave diagrams with computed flow properties are
courtesy of NASA.
Wave Rotor
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Simulation of Gas Flow in Complex Geometries
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Computational Mechanics Akay, Pidaparti, Chen,
Hsu, Paydar

• Computational simulations with ANSYS, ABAQUS, and
  PATRAN for modeling of complex problems
• Electronic Package Reliability
• Biomechanics

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Fatigue Life Prediction of Solder Joints of
Electronic Packages

• Thermal cyclic loads
• Creep response of solder

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Different Types of Interconnections
Chip Carrier
Lead
Solder
Copper Pad
PWB
LLCC Type
LDCC Type
Chip
Overmold
Adhesive
Substrate
Solder ball
PWB
Copper pad
BGA Type
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Finite Element Models
Y
X
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Predicted Fatigue Lives
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Biomechanics Chen, Hsu, Yokota, Turner, Katona

• Dental mechanics
• implants
• Orthopedics
• implants
• Blood flow
• heart valves

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Finite Element Model of aTooth-Mandible Structure

• Objective
• Investigation of the optimal force system that
  results in a prescribed tooth movement

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Numerical Simulation of Blood Flow Through
Mechanical Heart Valves
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Biomolecular Engineering - Yokota

• Development of Devices and Techniques for
  Biomolecular Uses
• Imaging DNA and protein molecules
• Modeling human genomics
• Analysis of Mechanical Effects on Tissue
  Integrity
• Modeling mechanical effects
• Evaluating physical therapy

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DNA complexes and Protein Molecules Imaged by
Atomic Force Microscopy
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Advanced Materials Pidaparti

• Fatigue and Fracture Predictions
• Computational Intelligence Applications
• Advanced Composites (man made and biological)
• Tools Used
• Computational - FEA and Analytical
• Mechanical Testing - Static, Fatigue and NDE
• Intelligent Models - Neural Networks, Fuzzy
  Logic, Artificial Intelligence

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Damage Assessment System
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Corrosion Damage Identification and
Quantification
Eddy Current
Ultrasound
Color Index
0-5 material Loss
5-10 material loss
10-15 material loss
-
15-20 material loss
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FRACTURE MECHANICS
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Advanced Engineering and Manufacturing Lab
Chen and El-Mounayri

• CAD/CAE/CAM/PDM integration
• Power train simulations (hybrid or conventional)
• Product data management
• Intelligent machining
• CAD/CAM
• Solid modeling
• Finite element modeling

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Advanced Engineering and Manufacturing

• Optimization of the tool path
• Machining process control
• Simulation, optimization, and automation
• Virtual product development, evaluation and
  assessment
• Study of various aspects of machining (e.g.,
  Accuracy, surface finish, and chatter)

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Advanced Engineering and Manufacturing Training
to Industry

• CAD/CAM (Pro/ENGINEER, UNIGRAPHICS, I-DEAS)
• Operation of CNC machines and NC programming
• Geometric Dimensioning and Tolerancing
• Machining process modeling, analysis, control and
  optimization

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Integrated Power Train Model for HV (power train
performance evaluation)
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Integrated CAD/CAE/CAM/PDM System
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Transmission Assembly
CAD MODELS
Casting Mold Design

• Gate
• Overflow
• Cooling line
• Part
• Box

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Machining Process Modeling, Simulation and
Optimization (El-Mounayri)

• Modeling and Simulation of End Milling
• Introduction
• Industry needs
• Process control through predictive models
• ANN models
• Optimization of the cutting conditions
• Optimization through modeling and simulation
• Validation and benefits
• Optimization through measurements

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ANN Based Models

• Artificial Neural Networks is used to more
  effectively map the relevant machining parameters
  to the process outputs of interest.
• Advantages/Characteristics
• No assumptions on the functional relationship
  (e.g., quadratic, cubic etc.) are imposed. ANN
  based models are the more natural and accurate
  models for representing the metal cutting
  process.
• ANN Models are able to accurately predict the
  outputs for inputs which lie outside the range of
  training.
•  

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An ANN Based Model
   

•  
•  

Predictive ANN force model Network
Topology Model used to optimize the cutting
conditions for minimum production cost
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Validation of the ANN Model
Simulation versus Experimental Force
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Application in IndustryOptimization of Pocketing
Geometric analysis of pocket milling cut
Design model
 
Various immersions and associated machining
parameters
Workpiece and toolpath
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Cutting Forces before Optimization
Force predicted using ANN model
Force measured experimentally
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Cutting Forces after Optimization
Force measured experimentally
Force predicted using ANN model
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Effect of Process Optimization35 reduction in
machining time and 40 in forces
Measured force after optimization (Using optimum
cutting conditions)
Measured force before optimization (Using
conservative cutting conditions)
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Experimental Setup for measuring Cutting Forces

• Experimental set-up for measuring cutting forces

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THE END!