Design space exploration for a scramjet engine by using data mining andlow-fidelity design techniques

Ali Can Ispir, Bora O. Cakir, Bayindir H. Saracoglu

DOI Number XXX-YYY-ZZZ

Conference Number HiSST-2022-423

Scramjet propulsion systems offer seamless operation to the high-speed vehicles along their hypersonic
flight phase without carrying on-board oxidizer or any rotating component. To discuss the reliability and
feasibility of the entire system and trajectory, it is necessary to perform conceptual design exercises
which are based on reduced-order approaches and enable one to investigate effect of multiple parame-
ters. In this article, we created a multi-dimensional design space of a scramjet engine with five varying
design variables which are flight Mach number and altitude, intake wedge angle and exit Mach number,
and fuel-air equivalence ratio. We computed the intake and overall system performance parameters and
implemented Artificial neural network regression technique into the dataset to perform comprehensive
exploration the design space of the engine under investigation.

Read the full paper here

artificial neural network, high-speed air-breathing propulsion, hypersonics, Scramjet

In Categories: 1.Events, HiSST 2022, Propulsion Systems and Components

The paper above was part of proceedings of a CEAS event and as such the author has signed a publication agreement to have their paper published in the repository. In the case this paper is found somewhere else CEAS always links to the other source. CEAS takes great care in making the correct content available to the reader. If any mistakes are found in the listings please contact us directly at papers@aerospacerepository.org and we will correct the listing promptly. CEAS cannot be held liable either for mistakes in editorial or technical aspects, nor for omissions, nor for the correctness of the content. In particular, CEAS does not guarantee completeness or correctness of information contained in external websites which can be accessed via links from CEAS’s websites. Despite accurate research on the content of such linked external websites, CEAS cannot be held liable for their content. Only the content providers of such external sites are liable for their content. Should you notice any mistake in technical or editorial aspects of the CEAS site, please do not hesitate to inform us.