Efficient multiscale methodology for local stress analysis of metallic railway bridges based on modal superposition principles

This paper presents an advanced submodelling methodology for local stress analysis of complex details of existing metallic railway bridges. The fatigue assessment of connections of large structures based on local methods leads inherently to a multiscale problem that can only be solved by adopting efficient numerical procedures. Aiming to overcome such limitations that influence the analysis process, submodelling techniques and modal superposition principles are combined to fully represent numerically the local geometrical, material and contact properties of the fatigue-critical details. The results of experimental in situ tests are proposed to characterise the numerical models and respective multiscale relation, implementing optimisation and validation procedures. In this work, the suggested efficient multiscale methodology for stress analysis aims to allow the subsequent local fatigue assessment, according to the real mechanism of loading transference, reducing sources of conservatism. All numerical procedures and respective validation thru experimental techniques are illustrated using a real case study.This work was financially supported by: Base Funding - UIDB/04708/2020 of the CONSTRUCT - Institute of R&D In Structures and Construction - funded by national funds through the FCT/MCTES (PIDDAC) and by national funds through FCT - Fundação para a Ciência e a Tecnologia; PD/BD/114101/2015. This work was also carried out in the framework of Shift2Rail projects IN2TRACK2 [826255-H2020-S2RJU-CFM-2018] and IN2TRACK3 [101012456-H2020-S2RJU-CFM-2020].info:eu-repo/semantics/publishedVersio

Advanced Approaches Applied to Materials Development and Design Predictions

This thematic issue on advanced simulation tools applied to materials development and design predictions gathers selected extended papers related to power generation systems, presented at the XIX International Colloquium on Mechanical Fatigue of Metals (ICMFM XIX), organized at University of Porto, Portugal, in 2018. In this issue, the limits of the current generation of materials are explored, which are continuously being reached according to the frontier of hostile environments, whether in the aerospace, nuclear, or petrochemistry industry, or in the design of gas turbines where efficiency of energy production and transformation demands increased temperatures and pressures. Thus, advanced methods and applications for theoretical, numerical, and experimental contributions that address these issues on failure mechanism modeling and simulation of materials are covered. As the Guest Editors, we would like to thank all the authors who submitted papers to this Special Issue. All the papers published were peer-reviewed by experts in the field whose comments helped to improve the quality of the edition. We also would like to thank the Editorial Board of Materials for their assistance in managing this Special Issue

A brief review of fatigue design criteria on offshore wind turbine support structures

In this paper, a brief review of the main fatigue design criteria and some advanced fatigue approaches applied to offshore structures (e.g. offshore wind turbines) are presented. It is extremely important to understand the fatigue phenomenon and how it affects structures since offshore structures are constantly submitted to cyclic loading and corrosive attacks that aggravate the problem. All the influencing factors and approaches used during the design phase are also discussed

Fatigue Crack Propagation Rates Prediction Using Probabilistic Strain‐Based Models

This chapter proposes an evaluation and extension of the UniGrow model to predict the fatigue crack propagation rate, based on a local strain-based approach to fatigue. The UniGrow model, classified as a residual stress‐based crack propagation model, is here applied to derive probabilistic fatigue crack propagation fields (p-da/dN-ΔK-R fields) for P355NL1 pressure vessel steel, covering distinct stress R-ratios. The results are compared with available experimental data. The required strain-life data are experimentally achieved and evaluated. The material representative element size, ρ*, a key parameter in the UniGrow model, is assessed by means of a trial-and-error procedure of inverse analysis. Moreover, residual stresses are computed for varying crack lengths and minimum-to-maximum stress ratios. Elastoplastic stress fields around the crack apex are evaluated with analytical relations and compared with elastoplastic finite-element (FE) computations. The deterministic strain-life relations proposed in the original UniGrow model are replaced by the probabilistic strain‐life fields (p-ε-N) proposed by Castillo and Canteli. This probabilistic model is also extended by considering a damage parameter to allow for mean stress effects. In particular, a probabilistic Smith-Watson-Topper field (p-SWT-N), alternatively to the conventional p-ε-N field, is proposed and applied to derive the probabilistic fatigue crack propagation fields

Literature review of mobile learning systems

This paper presents a research about the monotonic and failure behaviours of bolted joints made of thin plates of S350GD and S355MC steels grades, which are used for rack structures. A full factorial test matrix was performed considering two joints configurations (1+1 and 4+4 double shear bolted joints), two material thicknesses (2 and 3mm), three coatings (“black steel”, zinc coating, zinc plus paint) and two preload levels (25%×70%Fu and 70%Fu). Tests were performed under static monotonic loading until failure. Slip tests were also performed according to the EN 1090-2 standard to evaluate joint slip factors for the three material surface conditions. In addition to the experimental tests, numerical simulation of static tests were performed using elastoplastic material behaviour, based on Mises yield theory and isotropic hardening identified with experimental tensile testing data. The models were able to reproduce conveniently the ultimate loads of the joints and failure modes, including clamping and friction effects

A comparison between S-N Logistic and Kohout-Věchet formulations applied to the fatigue data of old metallic bridges materials

A new formulation of a Logistic deterministic S-N curve is applied to fatigue data of metallic materials from ancient Portuguese riveted steel bridges. This formulation is based on a modified logistic relation that uses three parameters to fit the low-cycle- (LCF), finite-life- and high-cycle-fatigue (HCF) regions. This model is compared to the Kohout-Věchet fatigue model, which has a refined adjustment from very low-cycle fatigue (VLCF) to very high-cycle fatigue (VHCF). These models are also compared with other models, such as, Power law and fatigue-life curve from the ASTM E739 standard. The modelling performance of the S-N curves was made using the fatigue data considering the stress fatigue damage parameter for the materials from the Eiffel, Luiz I, Fão and Trezói riveted steel bridges. Using a qualitative methodology of graphical adjustment analysis and another quantitative using the mean square error, it was possible to evaluate the performance of the mean S-N curve formulation. The results showed that the formulation of the S-N curve using the Logistic equation applied to the metallic materials from the old bridges obtained superior performance to the analysed models, both in the estimation of fatigue behaviour in the low-cycle fatigue (LCF) region and in the lowest mean square error

GA-BP Neural Network-Based Strain Prediction in Full-Scale Static Testing of Wind Turbine Blades

This paper proposes a strain prediction method for wind turbine blades using genetic algorithm back propagation neural networks (GA-BPNNs) with applied loads, loading positions, and displacement as inputs, and the study can be used to provide more data for the wind turbine blades’ health assessment and life prediction. Among all parameters to be tested in full-scale static testing of wind turbine blades, strain is very important. The correlation between the blade strain and the applied loads, loading position, displacement, etc., is non-linear, and the number of input variables is too much, thus the calculation and prediction of the blade strain are very complex and difficult. Moreover, the number of measuring points on the blade is limited, so the full-scale blade static test cannot usually provide enough data and information for the improvement of the blade design. As a result of these concerns, this paper studies strain prediction methods for full-scale blade static testing by introducing GA-BPNN. The accuracy and usability of the GA-BPNN prediction model was verified by the comparison with BPNN model and the FEA results. The results show that BPNN can be effectively used to predict the strain of unmeasured points of wind turbine blades