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Optimization and Computational Fluid Dynamics - Department of ...

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100 Kyriakos C. Giannakoglou <strong>and</strong> Dimitrios I. Papadimitriou<br />

Functional Value<br />

1<br />

1e-005<br />

1e-010<br />

1e-015<br />

1e-020<br />

1e-025<br />

Quasi Newton<br />

Conjugate Gradient<br />

Steepest Descent<br />

1e-030<br />

0 50 100 150 200 250<br />

(a)<br />

Functional Value<br />

Cycles<br />

1<br />

1e-005<br />

1e-010<br />

1e-015<br />

1e-020<br />

1e-025<br />

Functional Value<br />

1<br />

1e-005<br />

1e-010<br />

1e-015<br />

1e-020<br />

1e-025<br />

1e-030<br />

0 20 40 60 80 100 120 140<br />

Newton<br />

Quasi Newton<br />

1e-030<br />

0 10 20 30 40 50 60 70<br />

Equivalent Flow Solutions<br />

(c)<br />

Newton<br />

Quasi Newton<br />

(b)<br />

Cycles<br />

Fig. 4.3 Inverse design <strong>of</strong> a 2D duct (a) convergence rates <strong>of</strong> the three gradient-based<br />

algorithms (steepest descent, conjugate gradient <strong>and</strong> quasi-Newton) (b) convergence <strong>of</strong><br />

Newton <strong>and</strong> quasi-Newton algorithms in terms <strong>of</strong> optimization cycles c convergence <strong>of</strong><br />

Newton <strong>and</strong> quasi-Newton algorithms in terms <strong>of</strong> required equivalent flow solutions<br />

losses used as objective function <strong>and</strong> the corresponding adjoint equations are<br />

solved for the computation <strong>of</strong> the gradient. The sensitivity <strong>of</strong> the turbulent<br />

viscosity is not taken into account in the adjoint formulation. Geometrical<br />

constraints are imposed in order to prevent the blade shape from becoming<br />

too thin. The gradient <strong>of</strong> the constraint is added to the gradient <strong>of</strong> F,using<br />

the augmented Lagrange multipliers method [10].<br />

Each blade side is parameterized with 13 control points using Bézier-<br />

Bernstein polynomials [19]. Only the blade-to-blade coordinates <strong>of</strong> the control<br />

points are allowed to vary. The first three control points (determining<br />

the curvature <strong>of</strong> the leading edge) <strong>and</strong> the last one are kept constant on both<br />

sides. The H-type structured grid, chosen after a grid-independency study,<br />

has 400×200=80, 000 nodes. The flow conditions are: α1 =44 ◦ , M2,is =0.45<br />

<strong>and</strong> the chord-based Reynolds number is Rec =8×10 5 . The grid together<br />

with the Mach number distribution over the optimal cascade airfoil is shown<br />

in Fig. 4.6.<br />

The convergence history <strong>of</strong> F (total pressure losses) is shown in Fig. 4.7(a).<br />

In Fig. 4.7(b), the convergence history <strong>of</strong> the sum <strong>of</strong> violated constraints is

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