Imperial College London
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Luca Magri

Faculty of Engineering, Department of Aeronautics

Professor of Scientific Machine Learning
 
 
 
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Contact

 

l.magri Website

 
 
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Location

 

CAGB324City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Magri:2019:10.1016/j.jcp.2019.03.032,
author = {Magri, L},
doi = {10.1016/j.jcp.2019.03.032},
journal = {Journal of Computational Physics},
pages = {454--461},
title = {Adjoint characteristic decomposition of one-dimensional waves},
url = {http://dx.doi.org/10.1016/j.jcp.2019.03.032},
volume = {388},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - © 2019 Elsevier Inc. 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.
AU  - Magri,L
DO  - 10.1016/j.jcp.2019.03.032
EP  - 461
PY  - 2019///
SN  - 0021-9991
SP  - 454
TI  - Adjoint characteristic decomposition of one-dimensional waves
T2  - Journal of Computational Physics
UR  - http://dx.doi.org/10.1016/j.jcp.2019.03.032
VL  - 388
ER  -
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