This study investigates the temperature effect on the impedance of conventional singledegree-of-freedom liners, both without and with grazing flow. Experiments are performed in a controlled environment, with a detailed monitoring of the temperature all along the liner sample. The liner impedance is either derived from the reflection coefficient measured in a normal impedance tube, or is educed with an inverse method from acoustic velocity or wall pressure fields measured in the ONERA grazing flow duct. The influence of the acoustic source level on the temperature of the sample is also addressed, which enlights strong multiphysics coupling between acoustics, flow and thermal phenomena.

In the framework of civil aviation noise levels are becoming restricted every year, on one hand to provide comfort to the passengers and on the other hand to be compliant with regulations protecting airports surroundings. New technologies are required to reduce noise to cope with this restrictions as well as to guarantee a comfortable flight for passengers. For technological industries it is compulsory to stay competitive and keep improving the technology related to air intake acoustic liners. With an unceasingly growing market, for industries it is key to stay in the vanguard of air inlet technologies, ensuring innovation and establishing a proactive environment for future product generations. One of the main objectives in this framework is the reduction of the development time of these new technologies in all the stages of the process. In this work we focus on the design stage of a new prototype and we propose a hybrid technique enabling faster design and the reduction of development time. When designing new technologies or prototypes there are usually two constraints. On one hand, more innovative prototypes may present unconventional shapes are not accurately represented by conventional physical models. On the other hand, the available data is scarce, thus limiting the use of most innovative techniques based on the state-of-art of Artificial Intelligence. In this paper we propose a solution laying in the hybrid twin paradigm, combining both, data in the low limit and physics to provide a hybrid model able to represent unconventional and innovative acoustic solutions.