3 research outputs found
Adjoint characteristic decomposition of one-dimensional waves
Adjoint methods enable the accurate calculation of the sensitivities of a
quantity of interest. The sensitivity is obtained by solving the adjoint
system, which can be derived by continuous or discrete adjoint strategies. In
acoustic wave propagation, continuous and discrete adjoint methods have been
developed to compute the eigenvalue sensitivity to design parameters and
passive devices (Aguilar, J. G. et al, 2017, J. Computational Physics, vol.
341, 163-181). In this short communication, it is shown that the continuous and
discrete adjoint characteristic decompositions, and Riemann invariants, are
connected by a similarity transformation. The results are shown in the Laplace
domain. The adjoint characteristic decomposition is applied to a
one-dimensional acoustic resonator, which contains a monopole source of sound.
The proposed framework provides the foundation to tackle larger acoustic
networks with a discrete adjoint approach, opening up new possibilities for
adjoint-based design of problems that can be solved by the method of
characteristics
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Adjoint characteristic decomposition of one-dimensional waves
Adjoint methods enable the accurate calculation of the sensitivities of a quantity of
interest. The sensitivity is obtained by solving the adjoint system, which can be derived
by continuous or discrete adjoint strategies. In acoustic wave propagation, continuous and
discrete adjoint methods have been developed to compute the eigenvalue sensitivity
to design parameters and passive devices (Aguilar et al., 2017, [1]). In this short
communication, it is shown that the continuous and discrete adjoint characteristic
decompositions, and Riemann invariants, are connected by a similarity transformation.
The results are shown in the Laplace domain. The adjoint characteristic decomposition
is applied to a one-dimensional acoustic resonator, which contains a monopole source of
sound. The proposed framework provides the foundation to tackle larger acoustic networks
with a discrete adjoint approach, opening up new possibilities for adjoint-based design of
problems that can be solved by the method of characteristics
Adjoint characteristic decomposition of one-dimensional waves
Adjoint methods enable the accurate calculation of the sensitivities of a quantity of interest. The sensitivity is obtained by solving the adjoint system, which can be derived by continuous or discrete adjoint strategies. In acoustic wave propagation, continuous and discrete adjoint methods have been developed to compute the eigenvalue sensitivity to design parameters and passive devices (Aguilar et al., 2017, [1]). In this short communication, it is shown that the continuous and discrete adjoint characteristic decompositions, and Riemann invariants, are connected by a similarity transformation. The results are shown in the Laplace domain. The adjoint characteristic decomposition is applied to a one-dimensional acoustic resonator, which contains a monopole source of sound. The proposed framework provides the foundation to tackle larger acoustic networks with a discrete adjoint approach, opening up new possibilities for adjoint-based design of problems that can be solved by the method of characteristics