Introduction to Optimum Design phần 8 pot

... 1300 156 3 .83 0 W36 ¥ 280 82 .40 36.52 0 .88 5 16.595 1.570 189 00 1030 15.10 1200 144 3 .81 0 W36 ¥ 260 76.50 36.26 0 .84 0 16.550 1.440 17300 953 15.00 1090 132 3. 780 W36 ¥ 245 72.10 36. 08 0 .80 0 16.510 ... Applications involving 526 INTRODUCTION TO OPTIMUM DESIGN TABLE 15-3 Some Wide Flange Standard Sections Section A d t w bt f I x S x r x I y S y r y W36 ¥ 300 88 .30 36.7...

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... trial design point is (n) The cost and constraint functions at the new trial design point are calculated as (o) ff g 11 2 1 52 8 0 725 52 8 320 52 8 0 725 15 037 52 8 0 725 52 8 60 0 725 1 0 21 38 ... that has worked fairly well is 388 INTRODUCTION TO OPTIMUM DESIGN Table 11-1 Simplex Solution Procedure for QP Problem of Example 11.2 X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 Y...

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... È Î Í Í Í ˘ ˚ ˙ ˙ ˙ Kx x U 10 03 3 1214 9 3642 87 86 1 28 585 0 28 4540 3 300 28 585 0 28 4540 3 300 . E ∂ () ∂ = -+ -+ + È Î Í Í Í ˘ ˚ ˙ ˙ ˙ Kx U d 3 1214 03 2 85 85 04 2 85 85 04 . . . E E E ∂ ∂ = - È Î Í Í Í ˘ ˚ ˙ ˙ ˙ ∂ ∂ = - È Î Í Í Í ˘ ˚ ˙ ˙ ˙ g g 1 1 4 ... 4.3230E-01 9. 083 7E+00 8 CCC 3.53358E-02 1.67623E-02 1.19 085 E-02 5.2692E-02 3 .88 96E-01 9. 082 8E+00 9 CCC 4...

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... Minimization Subproblem 282 8. 2.2 Analytical Method to Compute Step Size 283 8. 2.3 Concepts Related to Numerical Methods to Compute Step Size 285 8. 2.4 Equal Interval Search 286 8. 2.5 Alternate Equal ... Algorithm 579 18. 4.2 Domain Elimination Method 580 18. 4.3 Stochastic Zooming Method 582 18. 4.4 Operations Analysis of the Methods 583 18. 5 Numerical Performance of...

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... as Plotg4=ContourPlot[g4,{x1,-1,25},{x2,-1,25}, ContourShadingÆFalse, ContoursÆ{0,.35}, ContourStyleÆ{{Thickness[.01]}, {GrayLevel[ .8] ,Thickness[.02]}}, DisplayFunctionÆIdentity]; 62 INTRODUCTION TO OPTIMUM ... of a design problem may not be linear, in which case curves must be plotted to identify the feasible region, and contours or iso-cost curves must be drawn to identify t...

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... changes in the cost function. b bd bd- ¥ () ==¥ () 480 10 0 4 80 10 8 3 23 8 . ;.or d bd bd- ¥ () ==¥ () 240 10 0 2 40 10 8 22 23 8 . ;.or Optimum Design Concepts 165 4.6.2 Convex Functions Consider ... df df ds su dg ds s j j * * == df ds f g dg ds u dg ds j j j j xx** * () = ∂ () ∂ = ∂ () ∂ == f g uj m j j x* * , 1to 1 48 INTRODUCTION TO OPTIMUM DESIGN As in Example...

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... 4x 2 subject to x 1 + 2x 2 £ 5 2x 1 + x 2 = 4 254 INTRODUCTION TO OPTIMUM DESIGN 232 INTRODUCTION TO OPTIMUM DESIGN 1. For 2x 1 + x 2 £ 9: y 1 = 1.6 (c¢ 3 in column x 3 ) 2. For x 1 - 2x 2 £ 2: y 2 = 1 .8 ... 20x 1 - 6x 2 subject to 3x 1 - x 2 ≥ 3 Linear Programming Methods for Optimum Design 247 2 28 INTRODUCTION TO OPTIMUM DESIGN TABLE 6-17 Continued Third...

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... method. 8. 41 Exercise 8. 21 8. 42 Exercise 8. 22 8. 43 Exercise 8. 23 8. 44 Exercise 8. 24 8. 45 Exercise 8. 25 8. 46 Exercise 8. 26 8. 47 Exercise 8. 27 8. 48 Exercise 8. 28 8.49 Exercise 8. 29 8. 50 Exercise 8. 30 Section ... direction. 8. 31 Exercise 8. 21 8. 32 Exercise 8. 22 8. 33 Exercise 8. 23 8. 34 Exercise 8. 24 8. 35 Exercise 8. 25 8. 36 Exercise 8....

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... Optimum Design 591 18. 6 (Hock and Schittkowski, 1 981 ) minimize subject to 18. 7 (Hock and Schittkowski, 1 981 ) minimize subject to 18. 8 (Hock and Schittkowski, 1 981 ) minimize subject to xx 21 2 125 0 -≥ xx 12 700 ... (1. 281 4E-03, 3.4050E-04), (2.2660E-07, 1.6638E-06), (2.5645E-01, 3.5256E-05). 18. 9 (Hock and Schittkowski, 1 981 ) minimize subject to 18. 10 (Hock and Sc...

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... f( a ) = 6 .88 747 ¥ 10 9 a 4 - 3.6111744 ¥ 10 8 a 3 + 5 .80 9444 ¥ 10 6 a 2 - 2 784 4 a + 41. 8. 24 f( a ) = 8 a 2 - 8 a + 2. 8. 25 f( a ) = 18. 5 a 2 - 85 a - 13.5. 8. 26 f( a ) = 288 a 2 - 96 a + 8. 8. 27 ... 0.0 280 ). 8. 57 x (2) = (0.259, -0.225, 0.145). 8. 58 x (2) = (4.2 680 , 0.2244). 8. 59 x (2) = (3 .84 15, 0. 480 87). 8. 60 x (2) = (-1.590, 2.592). 8. 61 x (2)...

Ngày tải lên: 13/08/2014, 18:20