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Optimization and Computational Fluid Dynamics - Department of ...

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4 Adjoint Methods for Shape <strong>Optimization</strong> 95<br />

Hessian as well. However, if first-order sensitivity derivatives are computed<br />

using the adjoint approach, N pairs <strong>of</strong> additional systems <strong>of</strong> direct or adjoint<br />

equations must be solved for the computation <strong>of</strong> the Hessian matrix. So, the<br />

total cost to compute all quantities needed by Eq. (4.43) is equal to that <strong>of</strong><br />

1+2N equivalent flow solutions.<br />

The high cost <strong>of</strong> the previous approaches is due to the fact that the adjoint<br />

approach is used for the first derivative <strong>and</strong> therefore, dU values are not<br />

dbi<br />

available; as it will become clear below, these quantities are necessary in<br />

order to proceed to the computation <strong>of</strong> the Hessian matrix. For this reason,<br />

the direct approach to compute the gradient <strong>of</strong> F must be employed. Given<br />

this decision, one should investigate the expected gain from the subsequent<br />

use <strong>of</strong> the adjoint approach to compute the Hessian (direct-adjoint approach)<br />

compared to the direct-direct approach, where the computation <strong>of</strong> the Hessian<br />

is based on the repeated application <strong>of</strong> the direct approach. It can be shown<br />

(this development is omitted) that the direct-direct approach requires N +<br />

N(N+1)<br />

2 equivalent flow solutions at each optimization cycle. On the other<br />

h<strong>and</strong>, the direct-adjoint approach, in which second derivatives are computed<br />

using the adjoint approach, is the less time-consuming one since it requires<br />

the solution <strong>of</strong> 1+N equivalent flow problems. Note that the previous figures<br />

must be augmented by one, so as to account for the cost <strong>of</strong> solving the flow<br />

equations <strong>and</strong> computing the value <strong>of</strong> F.<br />

4.6.1 Discrete Direct-adjoint Approach for the Hessian<br />

Starting from Eq. (4.1), the second derivative <strong>of</strong> F is given by<br />

d 2 F<br />

dbidbj<br />

= ∂2F +<br />

∂bi∂bj<br />

∂2F ∂bi∂Uk<br />

+ ∂2F dUk dUm<br />

∂Uk∂Um dbi dbj<br />

dUk<br />

dbj<br />

Equation (4.44) can be used to compute d2 F<br />

dbidbj<br />

ties) <strong>and</strong> d2 U<br />

dbidbj<br />

( N(N+1)<br />

2<br />

+ ∂2F dUk<br />

∂Uk∂bj dbi<br />

+ ∂F<br />

∂Uk<br />

d 2 Uk<br />

dbidbj<br />

provided that dU<br />

dbi<br />

. (4.44)<br />

(N quanti-<br />

quantities) can also be computed. The first derivatives<br />

<strong>of</strong> U are computed at the cost <strong>of</strong> N equivalent flow solutions, Eq. (4.2),<br />

according to the so-called direct approach. For the additional N(N+1)<br />

2 quantities<br />

(second derivatives <strong>of</strong> U), from Eq. (4.2), we get<br />

d 2 Rn<br />

dbidbj<br />

= ∂2Rn +<br />

∂bi∂bj<br />

∂2Rn ∂bi∂Uk<br />

+ ∂2Rn dUk<br />

∂Uk∂Um dbi<br />

dUm<br />

dbj<br />

dUk<br />

dbj<br />

+ ∂2Rn dUk<br />

∂Uk∂bj dbi<br />

+ ∂Rn<br />

∂Uk<br />

d 2 Uk<br />

=0 . (4.45)<br />

dbidbj

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