E Resta

M Ferlauto

R Marsilio

English

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

08 November 2022POLITECNICO DI TORINORepository ISTITUZIONALEAI Based Shock-Wave Detection by Using a Pressure Sensor Array / Resta, E; Ferlauto, M; Marsilio, R. -ELETTRONICO. - (2021). ((Intervento presentato al convegno ECCOMAS CSAI - Computational Sciences and AI inIndustry tenutosi a Virtual nel 7-9 June 2021.OriginalAI Based  Shock-Wave Detection by Using a Pressure Sensor  ArrayPublisher:PublishedDOI:Terms of use:openAccessPublisher copyright(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2958906 since: 2022-03-19T06:43:47ZCIMNE   International Centre for Numerical Methods in Engineering, Barcelona, SpainAI  BASED SHOCK-WAVE DETECTION BY USING A PRESSURESENSOR ARRAY Emanuele Resta  1 *, Michele Ferlauto1,  Roberto Marsilio11DIMEAS, Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, Italy* emanuele.resta@studenti.polito.itThe continuous expansion of intelligent systems has increased the demand and developmentof sensor devices with self-diagnostic and prognostic features [1-3], often working in full-authority control systems. Moreover, new diagnostics and health monitoring applications, aswell as the development of Digital Twin concepts push toward the use of large numbers ofreal/virtual sensors embedded in the system to be monitored. The use of spatially distributedsensors allows to extract information of very complex nature. In the proposed case the focusis on the detection and location of a shock-wave inside a supersonic duct. Supersonic aerospace propulsion systems deal with shocks inside the air-intake, which arecharacterized  by  nonlinear  dynamics  [4].  The  shock  must  be  actively  controlled,  byminimizing  intake losses and avoiding inlet unstart. Lower intake losses also mean narrowerstability margins for the shock. A robust active control requires an efficient and well resolveddetection of the actual shock position inside the air-intake. The general problem of detecting the position of a shock by using a discrete set of real orvirtual pressure sensors is studied numerically. A Machine Learning (ML) algorithm [5] istrained by CFD simulations of the transonic flow in a duct with an embedded shock-wave  atdifferent positions.  The shock-wave detection algorithm is then validated by using discretedata collected at  different  duct  locations,  to simulate the outputs of a  discrete  number ofpressure sensors placed along the duct. A noise model and randomly wrong pressure valuesare introduced in order to emulate the sensors’ outputs and faults. The algorithm effectivenessis analyzed for different sensor precisions.References[1]  A. Lerro et al., IEEE MetroAerospace 2019, Turin, Italy (2019).[2]  M. Wright and R. Horowitz, IEEE Trans. Intell. Trans. Sys. 17, 3577–3590 (2016).[3]  A. Provost, et al. IEEE MetroAeroSpace 2017, Padua, Italy  (2017).[4]  A. Singh, S. Medida, and K. Duraisamy, AIAA Journal 2215–2227 (2017).[5]  S. Nissen, “Implementation of a fast artificial neural network library (fann),” (2003).

AI Based  Shock-Wave Detection by Using a Pressure Sensor  Array

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AI Based Shock-Wave Detection by Using a Pressure Sensor Array

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