Enhanced Carbon Fiber-Epoxy Composites for Rowing Racing Shells

This thesis investigates the thermomechanical properties of two commercial composites using carbon fiber reinforcement in epoxy resins for manufacturing marine based rowing racing shells. The main goal of this project was to investigate how to control the resin properties and curing temperatures to improve the final product properties including adhesion, toughness modulus and tensile strength. Moreover, an efficient curing process was required by our supporting company to be used at low temperatures to enhance the curing characteristics and to provide improved mechanical properties. Accordingly, the current research tries to improve the manufacturing curing process and build up high performance structure with enhanced properties for low weight racing hulls. Using a vacuum bagging only technique (VBO), the composite prepregs were cured by an improved ramp rate of 3˚C/min. Numerous thermomechanical devices (e.t TGA, DSC, DMA and Instron) were used to check for weigh loss and mechanical properties of the carbon fiber- epoxy resin prepregs. The results of this thesis showed that utilizing the autoclave curing technique (OoA), an epoxy matrix composite could be prepared with the thermomechanical properties of the carbon fiber prepregs improved and the curing cycle shortened. A void- free and pinhole-free composite surface was obtained with enhanced mechanical properties using a ramp rate of 3˚C/min and holding time after the curing process of 2 hours and 50 minutes with an onset curing temperature of 121˚C. Keywords Carbon fiber- epoxy resin, composite prepregs, Vacuum bagging technique (VBO), TGA, DSC, DMA, Instron, Out of Autoclave curing (OoA

Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties

This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given stochastic material properties and geometric defects. A novel method of encoding geometric defects in an interpretable, resolution independent is showcased in the formation of input space probability distributions. Orders of magnitude sampling reductions down to $\sim10^0$ and $\sim10^1$ are achieved in the 1D and 7D input space scenarios respectively while maintaining accurate output space probability distributions through combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate model fitting

Traffic prediction using a self-adjusted evolutionary neural network

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior. On the other hand, an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks. This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective optimizer for short-term prediction. NSGA-II is used to optimize the number of neurons in the first and second layers of the NN, learning ratio and slope of the activation function. This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way. Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway. Results are analyzed based on the performance measures, showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment. The achieved prediction accuracy is calculated with multiple measures such as the root mean square error (RMSE), and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction, respectively. Document type: Articl

Approximate solutions of large amplitude vibration of a string

<div><p>Abstract In the present study, the response of a flexible string with large amplitude transverse vibration is studied utilizing amplitude-frequency formulation, improved amplitude-frequency formulation and max-min approach. In order to verify the accuracy of these approaches, obtained results are compared with other methods such as variational approach method, variational iteration method, coupling Newton’s method with the harmonic balance method and Hamiltonian approach. It has been found that for this problem, while amplitude-frequency formulation and max-min approach give the same results, improved amplitude frequency formulation is not an appropriate choice.</p></div

Multifunctional Hyperelastic Structured Surface for Tunable and Switchable Transparency

We leverage the crucial hyperelastic properties of a multifunctional structured surface to optimize the reconfigurability of the electromagnetic transmission under large nonlinear mechanical deformations. This multiphysics, multifunctional, hyperelastic structured surface (HSS) offers two simultaneous intriguing functionalities; tunability and switchability. It is made of copper resonators and a Polydimethylsiloxane (PDMS) substrate, which is one of the most favorable deformable substrates due to its hyperelastic behavior. The proposed HSS is fabricated via an original cost-effective technique and the multiphysics functionalities are captured in both experimental tests and numerical simulations. Leveraging the hyperelastic behavior, we demonstrate up to 8% percent shift in the resonance frequency in the GHz range, for average applied mechanical strains of around 17%. The hyperelastic deformations can continuously increase/decrease the magnitude of the scattering parameter S21 in the frequency range of 10.9 GHz to 11.8 GHz by more than 40 dB, changing from being largely transparent to opaque and vice versa. The potential of hyperelastic behavior to account for the multifunctionality of the HSS is validated experimentally

Fuzzy-based approach to quantify the downtime of buildings in developing countries

Earthquake is one of the natural disasters that affects the buildings and communities in developing countries. It causes different levels of damages to the buildings, making them uninhabitable for a period of time, called downtime (DT). This paper proposes a Fuzzy Logic hierarchical method to estimate the downtime of residential buildings in developing countries after an earthquake. The use of expert-based systems allows quantifying the indicators involved in the model using descriptive knowledge instead of hard data, accounting also for the uncertainties that may affect the analysis. The applicability of the methodology is illustrated using the information gathered after the 2015 Gorkha, Nepal, earthquake as a case study. On April 25, 2015, Nepal was hit by the Mw 7.8 Gorkha earthquake, which damaged and destroyed more than 500.000 residential buildings. Information obtained from a Rapid Visual Damage Assessment (RVDA) is used through a hierarchical scheme to evaluate the building damageability. Sensitivity analysis based on Sobol method is implemented to evaluate the importance of parameters gathered in the RVDA for building damage estimation. The findings of this work may be used to estimate the restoration time of damaged buildings in developing countries and to plan preventive safety measures

Additional file 1 of Inequality in the distribution of Covid-19 vaccine: a systematic review

Additional file 1: Appendix Table 1. Detailed information of selected studies