Stabilization of acoustic modes using Helmholtz and Quarter-Wave resonators tuned at exceptional points

Acoustic dampers are efficient and cost-effective means for suppressing thermoacoustic instabilities in combustion chambers. However, their design and the choice of their purging air mass flow is a challenging task, when one aims at ensuring thermoacoustic stability after their implementation. In the present experimental and theoretical study, Helmholtz (HH) and Quarter-Wave (QW) dampers are considered. A model for their acoustic impedance is derived and experimentally validated. In a second part, a thermoacoustic instability is mimicked by an electro-acoustic feedback loop in a rectangular cavity, to which the dampers are added. The length of the dampers can be adjusted, so that the system can be studied for tuned and detuned conditions. The stability of the coupled system is investigated experimentally and then analytically, which shows that for tuned dampers, the best stabilization is achieved at the exceptional point. The stabilization capabilities of HH and QW dampers are compared for given damper volume and purge mass flow.Comment: 34 pages, 19 figures, acepted in the Journal of Sound and Vibratio

Optimum reinforcement design of a passenger vehicle door panel to minimise vibrational deformation

Full-scale wind tunnel experiments and analysis using CFD (Computational Fluid Dynamics) are already developed and applied to the research and development processes of current passenger vehicles.(^1) But from the viewpoint of the indoor aspiration noise during high speed driving, the vibration of a passenger vehicle's door frame is a major influence. The vibrational deformation gives rise to aspiration noise, which is airborne sound transmitted through the gap between the door panel frame and the sealing system mounted on the body panel. The optimised design of a passenger vehicle’s door frame can lead us to the minimisation of aspiration noise. The optimisation is carried out by the finite element analysis of the vibration of the passenger vehicle's door panel assembly under steady-state sinusoidal dynamic air pressure. The commercial analysis package ABAQUS((^5-9)) is applied to all analyses in this thesis. The thesis concludes with recommendations for door reinforcement configurations to reduce aspiration wind noise, but such an optimum must be considered in relation to the associated financial costs and weight penalties

Effectiveness of resilient wheels in reducing noise and vibrations

This study focuses on the effectiveness of resilient wheels in reducing railway noise and vibrations, and compares the effectiveness of three types of wheels. The finite elements method has been used to characterise the vibratory behaviour of these wheels. The model has been excited with a realistic spectrum of vertical track irregularities, and a spectral analysis has been carried out. Results have been post-processed in order to estimate the sound power emitted. These calculations have been used to assess the effectiveness of the resilient wheel designs in reducing noise emitted to the environment and in propagating structural vibrations

Nonintrusive parametric solutions in structural dynamics

© 2022 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A nonintrusive reduced order method able to solve a parametric modal analysis is proposed in this work. The main objective is being able to efficiently identify how a variation of user-defined parameters affects the dynamic response of the structure in terms of fundamental natural frequencies and corresponding mode shapes. A parametric version of the inverse power method (IPM) is presented by using the proper generalised decomposition (PGD) rationale. The proposed approach utilises the socalled encapsulated PGD toolbox and includes a new algorithm for computing the square root of a parametric object. With only one offline computation, the proposed PGD-IPM approach provides an analytical parametric expression of the smallest (in magnitude) eigenvalue (or natural frequency) and corresponding eigenvector (mode shape), which contains all the possible solutions for every combination of the parameters within pre-defined ranges. A Lagrange multiplier deflation technique is introduced in order to compute subsequent eigenpairs, which is also valid to overcome the stiffness matrix singularity in the case of a free-free structure. The proposed approach is nonintrusive and it is therefore possible to be integrated with commercial finite element (FE) packages. Two numerical examples are shown to underline the properties of the technique. The first example includes one material and one geometric parameter. The second example shows a more realistic industrial example, where the nonintrusivity of the approach is demonstrated by employing a commercial FE package for assembling the FE matrices. Finally, a multi-objective optimisation study is performed proving that the developed method could significantly assist the decision-making during the preliminary phase of a new design process.This project is part of the Marie Skłodowska-Curie ITN-EJD ProTechTion funded by the European Union Horizon 2020 research and innovation program with Grant Number 764636. The work of Fabiola Cavaliere, Sergio Zlotnik and Pedro Díez is partially supported by the MCIN/AEI/10.13039/501100011033, Spain (Grant Number: PID2020-113463RB-C32, PID2020-113463RB-C33 and CEX2018-000797-S). Ruben Sevilla also acknowledges the support of the Engineering and Physical Sciences Research Council (Grant Number: EP/P033997/1).Peer ReviewedPostprint (author's final draft

Longitudinal Eigenvibration of Multilayer Colloidal Crystals and the Effect of Nanoscale Contact Bridges

Longitudinal contact-based vibrations of colloidal crystals with a controlled layer thickness are studied. These crystals consist of 390 nm diameter polystyrene spheres arranged into close packed, ordered lattices with a thickness of one to twelve layers. Using laser ultrasonics, eigenmodes of the crystals that have out-of-plane motion are excited. The particle-substrate and effective interlayer contact stiffnesses in the colloidal crystals are extracted using a discrete, coupled oscillator model. Extracted stiffnesses are correlated with scanning electron microscope images of the contacts and atomic force microscope characterization of the substrate surface topography after removal of the spheres. Solid bridges of nanometric thickness are found to drastically alter the stiffness of the contacts, and their presence is found to be dependent on the self-assembly process. Measurements of the eigenmode quality factors suggest that energy leakage into the substrate plays a role for low frequency modes but is overcome by disorder- or material-induced losses at higher frequencies. These findings help further the understanding of the contact mechanics, and the effects of disorder in three-dimensional micro- and nano-particulate systems, and open new avenues to engineer new types of micro- and nanostructured materials with wave tailoring functionalities via control of the adhesive contact properties

Damage identification in bridges combining deep learning and computational mechanic

xiv, 112 p.[EN]Civil infrastructures, such as bridges, are critical assets for society and the economy. Many of them have already reached their expected life and withstand loadings that exceed the design specifications. Besides, bridges suffer from various degradation mechanisms, including aging, corrosion, earthquakes, and, nowadays, the undeniable effect of climate change. This context has motivated an increasing interest in early detecting damage to prevent costly actions and dangerous failures. Structural Health Monitoring (SHM) consists of implementing effective strategies to continuously assess the health condition of structures using monitoring data collected by sensors. This dissertation focuses on the SHM problem of damage detection and identification. It is an ill-posed inverse problem that aims at inferring the health state of a structure from measurements of its response. The measurements include large amounts of noisy data affected by environmental and operational conditions, acquired with sensors of different nature. Solving such a multidisciplinary problem encompasses the use of applied mathematics, computational mechanics, and data science. In this dissertation, we exploit the potential of Deep Neural Networks in approximating complex inverse problems and employ computational parametrizations and the Finite Element Method to enrich the training phase by including damage scenarios. We explore two different approaches to the problem. In the first approach, we develop an outlier detection strategy to detect departures from the baseline condition. We only employ long-term monitoring data measured at the bridge during normal (healthy) operation. Starting from Principal Component Analysis (PCA) as a statistical data reconstruction technique, we design a specific Deep Autoencoder network that enhances PCA by adding residual connections to include nonlinear transformations. This architecture gains partial explainability by evaluating the contribution of nonlinearties over affine transformations in the reconstruction process. We also investigate the method performance when using local or global variables and evaluate the potential of combining both data sources in the damage detection task. In the second approach, we reach a higher level of damage identification by estimating its severity and location. The goal is to provide a suitable methodology for real full-scale applications that requires reasonable computational resources. We employ a calibrated computational parametrization to solve multiple Finite Element simulations under different damage scenarios. These synthetic scenarios enrich the training dataset of a Deep Neural Network that maps the response of the bridge with its health condition in terms of damage location and severity. Finally, we incorporate the effect of environmental and operational variability in the parametrization by applying a clustering algorithm to find representative samples among the entire dataset. We assume these samples cover most of the variability present in the data and consider them as starting points to generate synthetic training data. We apply the proposed methods to three main case study bridges with available monitoring data: the Beltran bridge in Mexico, and the Infante Dom Henrique bridge in Porto, and the Z24 bridge in Switzerland. Both structures resulted critical to validate and test the ability of the proposed methods and to demonstrate their applicability in the full-scale.[ES]Esta tesis investiga la aplicación de técnicas Deep Learning y Mecánica Computacional en el ámbito de identificación de daños estructurales en puentes. En primer lugar, abordamos técnicas basadas puramente en datos, que emplean únicamente la respuesta estructural adquirida mediante un sistema de instrumentación (sensores). Estas técncias proporcionan un diagnóstico de alerta (daño- no daño). Empleamos un tipo de red neuronal conocido como Autoencoder, al que dotamos de una arquitectura particular que pretende replicar transformaciones afines (como el Análisis de Componenetes Principales) e incorporar transormaciones no lineales de forma interpretable y comprensible. Con el objetivo de alcanzar un nivel más elevado en el diagnóstico, estudiamos una metodología híbrida que incorpora la mecánica computacional como fuente adicional de datos. Mediante el uso de una parametrización de elementos finitos, obtenemos la respuesta estructural sintética ante diferentes escenarios de daño, clasificados por su localización y su grado de severidad. Esta metodología require una calibración previa de la parametrización de acuerdo a un estado de referencia, y los escenarios generados se emplean para entrenar una red neuronal profunda capaz de estimar la localización y severidad de un daño cuando se obtienen nuevas mediciones en el sistema de instrumentación.This disseration has been possible thanks to the support received from: the European Union’s Horizon 2020 research and innovation program under the grant agreement No 769373 (FORESEE project) and the Marie Sklodowska-Curie grant agreement No 777778 (MATHROCKS); the Base Funding - UIDB/04708/2020 of the CONSTRUCT - Instituto de I&D em Estruturas e Constru¸c˜oes - funded by national funds through the FCT/MCTES (PIDDAC); the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program POCTEFA 2014-2020 Project PIXIL (EFA362/19); the Spanish Ministry of Science and Innovation with references PID2019-108111RB-I00 (FEDER/AEI) and the “BCAM Severo Ochoa” accreditation of excellence (SEV-2017-0718); and the Basque Government through the BERC 2018-2021 program, the four Elkartek projects 3KIA (KK-2020/00049), EXPERTIA (KK-2021/00048), MATHEO (KK-2019-00085), and SIGZE (KK-2021/00095); the grant “Artificial Intelligence in BCAM number EXP. 2019/00432”, and the Consolidated Research Group MATHMODE (IT1294-19) given by the Department of Education

Polymer cover induced self-excited vibrations of nipped rolls

Peer reviewe

Aeroelastic simulations of stores in weapon bays using Detached-Eddy simulation

Detached-Eddy Simulations of flows in weapon bays with a generic store at different positions in the cavity and with flexible fins are presented in this paper. Simulations were carried out to better understand the fluid–structure interactions of the unsteady, turbulent flow and the store. Mach and Reynolds numbers (based on the missile diameter) were 0.85 and 326.000 respectively. Spectral analysis showed few differences in the frequency content in the cavity between the store with rigid and flexible fins. However, a large effect of the store position was seen. When the store was placed inside the cavity, the noise reduction reached 7 dB close to the cavity ceiling. The closer the store to the carriage position, the more coherent and quieter was the cavity. To perform a more realistic simulation, a gap of 0.3% of the store diameter was introduced between the fin root and the body of the store. Store loads showed little differences between the rigid and flexible fins when the store was inside and outside the cavity. With the store at the shear layer, the flexible fins were seen to have a reduction in loads with large fluctuations in position about a mean. Fin-tip displacements of the store inside the cavity were of the range of 0.2% of the store diameter, and in the range of 1–2% of store diameter when at the shear layer