Wave-based numerical methods for damage identification in components and structures

Components and structures accumulate damage during operation, which degrades their load bearing capacity and is prone to causing catastrophic failure. The demand for fuel efficiency and reduction of pollutant emissions has shifted the design of many structures, predominantly aerospace, to incorporate more composite materials. Composite materials are especially susceptible to critical failure due to operation-induced and accidental damage modes, that have adverse impact on the material strength. Timely detection and identification of damage is important in ensuring structural integrity and safety. Continuous and reliable condition monitoring of components is even more important in lightweight structures that have lower loadbearing redundancy. Recent advances in sensors and signal processing, along with the availability of computational power, have rendered model-based monitoring and damage identification solutions attractive. Computational models for wave simulation remain, however, too heavy for conventional use. Robust and efficient modelling of certain damage modes, such as cracks, introduces additional complexities in numerical models for solids. Computational cost for inverse schemes, where multiple solutions for the unknown and sought damage parameters are required, even becomes prohibitive. This work introduces mesh-independent modelling of damage through XFEM, in wave analysis. The behaviour of damage is investigated with the developed method, and validated by established explicit Finite Element models. A signal processing methodology with wavelet transform is also implemented to further investigate the feasibility of wave scattering as means of damage identification, with a view over available wave actuation and measurement methods. The proposed methodology can achieve significant model reduction calculating wave scattering. Furthermore, identification of cracks is feasible, provided multiple wavemodes can be identified and measured

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results

D5.1 SHM digital twin requirements for residential, industrial buildings and bridges

This deliverable presents a report of the needs for structural control on buildings (initial imperfections, deflections at service, stability, rheology) and on bridges (vibrations, modal shapes, deflections, stresses) based on state-of-the-art image-based and sensor-based techniques. To this end, the deliverable identifies and describes strategies that encompass state-of-the-art instrumentation and control for infrastructures (SHM technologies).Objectius de Desenvolupament Sostenible::8 - Treball Decent i Creixement EconòmicObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraPreprin

Ultrasound for Material Characterization and Processing

Ultrasonic waves are nowadays used for multiple purposes including both low-intensity/high frequency and high-intensity/low-frequency ultrasound. Low-intensity ultrasound transmits energy through the medium in order to obtain information about the medium or to convey information through the medium. It is successfully used in non-destructive inspection, ultrasonic dynamic analysis, ultrasonic rheology, ultrasonic spectroscopy of materials, process monitoring, applications in civil engineering, aerospace and geological materials and structures, and in the characterization of biological media. Nowadays, it is an essential tool for assessing metals, plastics, aerospace composites, wood, concrete, and cement. High-intensity ultrasound deliberately affects the propagation medium through the high local temperatures and pressures generated. It is used in industrial processes such as welding, cleaning, emulsification, atomization, etc.; chemical reactions and reactor induced by ultrasonic waves; synthesis of organic and inorganic materials; microstructural effects; heat generation; accelerated material characterization by ultrasonic fatigue testing; food processing; and environmental protection. This book collects eleven papers, one review, and ten research papers with the aim to present recent advances in ultrasonic wave propagation applied for the characterization or the processing of materials. Both fundamental science and applications of ultrasound in the field of material characterization and material processing have been gathered

Damage Identification of Railway Bridges through Temporal Autoregressive Modeling

The damage identification of railway bridges poses a formidable challenge given the large variability in the environmental and operational conditions that such structures are subjected to along their lifespan. To address this challenge, this paper proposes a novel damage identification approach exploiting continuously extracted time series of autoregressive (AR) coefficients from strain data with moving train loads as highly sensitive damage features. Through a statistical pattern recognition algorithm involving data clustering and quality control charts, the proposed approach offers a set of sensor-level damage indicators with damage detection, quantification, and localization capabilities. The effectiveness of the developed approach is appraised through two case studies, involving a theoretical simply supported beam and a real-world in-operation railway bridge. The latter corresponds to the Mascarat Viaduct, a 20th century historical steel truss railway bridge that remains active in TRAM line 9 in the province of Alicante, Spain. A detailed 3D finite element model (FEM) of the viaduct was defined and experimentally validated. On this basis, an extensive synthetic dataset was constructed accounting for both environmental and operational conditions, as well as a variety of damage scenarios of increasing severity. Overall, the presented results and discussion evidence the superior performance of strain measurements over acceleration, offering great potential for unsupervised damage detection with full damage identification capabilities (detection, quantification, and localization).E. García-Macías was partially supported by the research project “SMART-BRIDGES-Monitorización Inteligente del Estado Estructural de Puentes Ferroviarios” (Ref. PLEC2021-007798) funded by the Spanish Ministry of Science and Innovation, the Spanish State Research Agency, and NextGenerationEU. F. Ubertini acknowledges the support of the Italian Ministry of University and Research (MUR) through the project of National Interest (PRIN PNRR 2022) “TIMING–Time evolution laws for IMproving the structural reliability evaluation of existING post-tensioned concrete deck bridges” (Prot. P20223Y947). The authors also acknowledge the regional administration of the Valencian Community in Spain for the financial support provided by the projects GRISOLIAAP/2019/122 and APOTIP/2021/003, and the European Union for the Project DESDEMONA Grant Agreement n. 800687. Finally, the authors would also like to express their gratitude to FGV (Ferrocarrils de la Generalitat Valenciana) and CALSENS S.L., for their invaluable cooperation and recommendations

Experimental and Computational Approaches to Optimizing Bovine Gamete Cryopreservation

Cryopreservation uses freezing to suspend the metabolic activity of biological specimens for increased longevity of biotic materials like gametes. Cryopreservation has been known to affect both the functionality (performance of activities) and the viability (survival) of biological specimens during the freezing and thawing process due to four types of damage: (1) thermal, (2) ice, (3) osmotic stress, and (4) cytotoxic. Cryoprotective agents (CPAs) are known to reduce thermal and ice damage but cause osmotic stress and cytotoxic damage. Osmotic stress occurs when the addition of CPAs causes a rapid expulsion of water from the cell as the extracellular environment has become hypertonic. Cytotoxic damage occurs when a cell is exposed for too long to CPAs that may be damaging to the cell at high temperatures, but aid in preservation at low temperatures. The purpose of my project is to minimize osmotic stress in bovine embryos and cytotoxic damage in bovine sperm caused by CPAs using novel algorithmically guided techniques. To minimize osmotic stress in bovine embryos, I aim to facilitate the equilibration of embryos with cryoprotective agents isochorically (constant volume). Isochoric cryoprotectant equilibration, requires a feedback control system that in our case will use real-time image analysis developed in this thesis to estimate current embryo volume and then adjusts the concentration of CPAs being administered to the system. I implemented a colour-based image analysis software that was able to process images of bovine embryos as they were exposed to CPAs at a sub-second rate. The sub-second processing rates include cell volume estimates that are comparable to manual cell volume estimates. To minimize cytotoxic damage in bovine sperm, I optimized cryopreservation media (CPM) composition to maximize post-thaw motility. The composition of CPM can contain many ingredients that have the potential to interact and are infeasible to test only empirically. Here, I combined empirical experiments, data-driven optimization algorithms, and machine learning to optimize the composition of CPM. I used differential evolution and Gaussian process regression to optimize CPM composition that are on par with commercial media after 9 iterations. During the optimization process I determined that Gaussian process regression model was superior to artificial neural networks when predicting post-thaw motility for a given CPM composition. By optimizing these cryopreservation processes, cellular damage can be reduced, improving functionality and viability of gametes used in assisted reproductive technology that can be applied across animal husbandry and biomedical fields