MAE550 PROJECT By: Lin Tom Tom Yan Yan Lv Lv. 1 Introduction OSA is a method of estimating the approximate effect that some change in problem parameters.

Slides:



Advertisements
Similar presentations
1 Radio Maria World. 2 Postazioni Transmitter locations.
Advertisements

EcoTherm Plus WGB-K 20 E 4,5 – 20 kW.
Números.
1 A B C
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
AGVISE Laboratories %Zone or Grid Samples – Northwood laboratory
Trend for Precision Soil Testing % Zone or Grid Samples Tested compared to Total Samples.
PDAs Accept Context-Free Languages
/ /17 32/ / /
Reflection nurulquran.com.
1
EuroCondens SGB E.
Worksheets.
Slide 1Fig 26-CO, p.795. Slide 2Fig 26-1, p.796 Slide 3Fig 26-2, p.797.
Slide 1Fig 25-CO, p.762. Slide 2Fig 25-1, p.765 Slide 3Fig 25-2, p.765.
Sequential Logic Design
Addition and Subtraction Equations
Multiplication X 1 1 x 1 = 1 2 x 1 = 2 3 x 1 = 3 4 x 1 = 4 5 x 1 = 5 6 x 1 = 6 7 x 1 = 7 8 x 1 = 8 9 x 1 = 9 10 x 1 = x 1 = x 1 = 12 X 2 1.
Division ÷ 1 1 ÷ 1 = 1 2 ÷ 1 = 2 3 ÷ 1 = 3 4 ÷ 1 = 4 5 ÷ 1 = 5 6 ÷ 1 = 6 7 ÷ 1 = 7 8 ÷ 1 = 8 9 ÷ 1 = 9 10 ÷ 1 = ÷ 1 = ÷ 1 = 12 ÷ 2 2 ÷ 2 =
David Burdett May 11, 2004 Package Binding for WS CDL.
Create an Application Title 1Y - Youth Chapter 5.
Add Governors Discretionary (1G) Grants Chapter 6.
CALENDAR.
CHAPTER 18 The Ankle and Lower Leg
Summative Math Test Algebra (28%) Geometry (29%)
2.11.
The 5S numbers game..
突破信息检索壁垒 -SciFinder Scholar 介绍
A Fractional Order (Proportional and Derivative) Motion Controller Design for A Class of Second-order Systems Center for Self-Organizing Intelligent.
Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)
Media-Monitoring Final Report April - May 2010 News.
Break Time Remaining 10:00.
The basics for simulations
PP Test Review Sections 6-1 to 6-6
Figure 3–1 Standard logic symbols for the inverter (ANSI/IEEE Std
TCCI Barometer March “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
1 Prediction of electrical energy by photovoltaic devices in urban situations By. R.C. Ott July 2011.
TCCI Barometer March “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
Copyright © 2012, Elsevier Inc. All rights Reserved. 1 Chapter 7 Modeling Structure with Blocks.
Progressive Aerobic Cardiovascular Endurance Run
Biology 2 Plant Kingdom Identification Test Review.
MaK_Full ahead loaded 1 Alarm Page Directory (F11)
TCCI Barometer September “Establishing a reliable tool for monitoring the financial, business and social activity in the Prefecture of Thessaloniki”
When you see… Find the zeros You think….
2011 WINNISQUAM COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=1021.
Before Between After.
2011 FRANKLIN COMMUNITY SURVEY YOUTH RISK BEHAVIOR GRADES 9-12 STUDENTS=332.
2.10% more children born Die 0.2 years sooner Spend 95.53% less money on health care No class divide 60.84% less electricity 84.40% less oil.
Subtraction: Adding UP
: 3 00.
5 minutes.
Numeracy Resources for KS2
1 Non Deterministic Automata. 2 Alphabet = Nondeterministic Finite Accepter (NFA)
Static Equilibrium; Elasticity and Fracture
Converting a Fraction to %
Resistência dos Materiais, 5ª ed.
Clock will move after 1 minute
Lial/Hungerford/Holcomb/Mullins: Mathematics with Applications 11e Finite Mathematics with Applications 11e Copyright ©2015 Pearson Education, Inc. All.
Physics for Scientists & Engineers, 3rd Edition
Select a time to count down from the clock above
1.step PMIT start + initial project data input Concept Concept.
1 Dr. Scott Schaefer Least Squares Curves, Rational Representations, Splines and Continuity.
Chart Deception Main Source: How to Lie with Charts, by Gerald E. Jones Dr. Michael R. Hyman, NMSU.
1 Non Deterministic Automata. 2 Alphabet = Nondeterministic Finite Accepter (NFA)
Introduction Embedded Universal Tools and Online Features 2.
Schutzvermerk nach DIN 34 beachten 05/04/15 Seite 1 Training EPAM and CANopen Basic Solution: Password * * Level 1 Level 2 * Level 3 Password2 IP-Adr.
Presentation transcript:

MAE550 PROJECT By: Lin Tom Tom Yan Yan Lv Lv

1 Introduction OSA is a method of estimating the approximate effect that some change in problem parameters has on the optimum design. For example, if materials or design requirements are changed after we have already found an optimum solution to the original problem, we wish to estimate the effect that this will have on the design without actually solving the optimization problem over again. OSA is a method of estimating the approximate effect that some change in problem parameters has on the optimum design. For example, if materials or design requirements are changed after we have already found an optimum solution to the original problem, we wish to estimate the effect that this will have on the design without actually solving the optimization problem over again.

There are two general approaches to the OSA problem: There are two general approaches to the OSA problem: o (1) Base on Kuhn-Tucker conditions. o (2) Use the concept of a feasible direction. In this project, we use the first one to obtain estimated optimum solution of cantilevered beam problem shown below (in the book, Page-184). In this project, we use the first one to obtain estimated optimum solution of cantilevered beam problem shown below (in the book, Page-184).

OSA Algorithm: OSA Algorithm: We only consider the active constraint

Then we can form the matrix: At last, we derive the optimum solution:

2 Cantilevered Beam Problem Figure 2-1 Minimize: Minimize: N=5, for conveniences, we assume each N=5, for conveniences, we assume each In this problem, we use optimum solution from Dot program (method 3) which is more accurate than the Master due to direct handling issues. In this problem, we use optimum solution from Dot program (method 3) which is more accurate than the Master due to direct handling issues.

Core Code List (Dot): Core Code List (Dot): P= E= L=500 Sigma=14000 Y=2.5 OBJ=0 DO 50 i=1,5 50 OBJ=OBJ+100*x(i)*x(i+5) G(1)=6.*P*L/(X(1)*X(6)*X(6)*Sigma)-1 G(2)=6.*P*(L-100)/(X(2)*X(7)*X(7)*Sigma)-1 G(3)=6.*P*(L-200)/(X(3)*X(8)*X(8)*Sigma)-1 G(4)=6.*P*(L-300)/(X(4)*X(9)*X(9)*Sigma)-1 G(5)=6.*P*(L-400)/(X(5)*X(10)*X(10)*Sigma)-1 G(6)=X(6)-20*X(1) G(7)=X(7)-20*X(2) G(8)=X(8)-20*X(3)

G(9)=X(9)-20*X(4) G(10)=X(10)-20*X(5) G(11)=1-X(1) G(12)=1-X(2)G(13)=1-X(3)G(14)=1-X(4)G(15)=1-X(5)G(16)=5-X(6)G(17)=5-X(7)G(18)=5-X(8)G(19)=5-X(9)G(20)=5-X(10)G(21)=(0.032*P*L*L*L/(X(5)*X(10)*X(10)*X(10))+ *0.144*P*L*L*L/(X(4)*X(9)*X(9)*X(9))+ *0.144*P*L*L*L/(X(4)*X(9)*X(9)*X(9))+ *0.608*P*L*L*L/(X(3)*X(8)*X(8)*X(8))+ *0.608*P*L*L*L/(X(3)*X(8)*X(8)*X(8))+ *1.184*P*L*L*L/(X(2)*X(7)*X(7)*X(7))+ *1.184*P*L*L*L/(X(2)*X(7)*X(7)*X(7))+ *1.936*P*L*L*L/(X(1)*X(6)*X(6)*X(6)))/Y/E-1 *1.936*P*L*L*L/(X(1)*X(6)*X(6)*X(6)))/Y/E-1

3 OSA Calculation We consider four factors(P, E, Sigma and Y),respectively, as the parameter P in OSA problem to see how they perform with approximation to objective function and to the constraints. We consider four factors(P, E, Sigma and Y),respectively, as the parameter P in OSA problem to see how they perform with approximation to objective function and to the constraints.

Load Load P=55,000 (10%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

P=60,000 (20%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

P=65,000 (30%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

Elastic modulus Elastic modulus E=22,000,000 (10%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) % E=24,000,000 (20%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

E=26,000,000 (30%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

Stress Stress Sigma=15,400 (10%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) % Sigma=16,800 (20%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

Sigma=18,200 (30%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

Deflection Deflection Y=2.75 (10%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) % Y=3 (20%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %

Y=3.25 (30%) ActualOSAError b % b % b % b % b % h % h % h % h % h % F(X) %