The performance and hydrodynamics of a Hybrid Wave Energy Converter (HWEC) device, combining Oscillating Water Column (OWC) and Overtopping Wave Energy Converter (OWEC) technologies, inserted into a 2:3 sloped breakwater, are investigated. HWEC and individual OWC and OWEC, subject to the action of incident regular waves with periods from 6 to 14 s and wave heights of 1, 2, and 3 m, are studied and compared. Investigations are carried out by using the ANSYS-FLUENT (R) software and adopting a numerical model based on the Reynolds-Averaged Navier-Stokes equations, the Volume of Fluid method for the free surface flow, and a hybrid kappa-omega SST turbulent/laminar model to deal with the involved complex phenomena. The HWEC device has higher efficiency than the individual OWC and OWEC devices and a more constant efficiency over the wave period interval, showing that both OWC and OWEC technologies complement each other and work well together. The maximum efficiency is achieved at the resonance period, around 9 s, reaching 77.6, 76.9, and 75.5% for incident wave heights of 1, 2, and 3 m, respectively. The energy balance analysis highlights that the HWEC device provides the best efficiency due to the limitation of the reflected wave energy and energy losses.
Fire accidents in road tunnels can cause a significant number of fatalities and severe damage to tunnel structures. The tunnel European directive applies to the trans-European road network and requires the use of active smoke control systems in most tunnels longer than 1000 m. Research has investigated whether shorter tunnels without active smoke control systems are safe. If smoke contaminates the lower layer where people evacuate, it can impair visibility. This disturbs egress and may cause intoxication and, eventually, death. The FireFoam computer code was applied to the Memorial Tunnel fire ventilation tests for validation. This work investigates the effect of varying the heat release rate (HRR), ranging from 6 to 100 MW, under a wind velocity of 0.77 m/s and in the absence of wind. Results show that high HRR moves the start of lower layer smoke contamination closer to the fire source, reducing the distance from 390 m at 14 MW to as close as 210 m at 100 MW. An analytical model was developed to predict the distance from the fire source where smoke can contaminate the lower layer and was subsequently improved to account for HRR variation.
Building on a previously developed bedrock dataset, this study extends the Azores Plateau ground motion simulations to include soil-amplified records and introduces a comprehensive validation framework. Soil amplification is modeled using one-dimensional soil profiles. A stochastic source-based approach is employed to generate the dataset, incorporating randomization of input-model parameters to account for the aleatory uncertainty in seismic activity. The accuracy of the dataset is verified through a comprehensive validation framework, showing that the randomization effectively captures variance and inter-period correlation observed in records. This work provides a robust dataset for advancing seismic hazard and risk assessment in the Azores Plateau.
This paper introduces a methodological framework for developing an autonomous system that minimizes human involvement within a measurement chain. The approach integrates data acquisition, metadata retrieval, and processing within a Machine-to-Machine (M2M) communication paradigm, thereby substantially minimizing manual operations throughout the workflow. To safeguard data integrity and detect unintended corruption during M2M exchanges, a data self-validation mechanism is introduced. Aligned with the Industry 5.0 vision-which emphasizes advanced data management and intelligent machine communication-the proposed approach contributes to the ongoing digital transformation of metrology, namely, through the implementation of Digital Calibration Certificates (DCC), promoting seamless and automated exchange of measurement data. The system was successfully applied to an experimental remote meteorological station, enabling fully autonomous measurement, validation, and data storage using DCC.
Understanding the durability of FRP composites under moisture exposure is important for civil engineering structures, as they are often used in wet environments and usually designed for relatively long service lives. Having validated degradation models can be extremely helpful in developing reliable durability design provisions. Currently, robust and comprehensive degradation models, validated by extensive experimental data sets, do not exist. In this context, the study presented in this paper is a preliminary contribution to developing such degradation models for FRP composites exposed to moisture. Data were collected from the literature on the impact of moisture exposure on different mechanical properties of FRP composites. The collected data encompasses mechanical property retention, characteristics of the composites and ageing protocols, such as constituent materials (type of fibre and resin), production method, thickness, fibre-volume ratio, shape, preconditioning, immersion temperature, ageing duration and loading condition. Degradation models were developed using different machine learning algorithms and optimized by tuning the hyperparameters. The Gradient Boosting algorithm provided the best performance (R2 = 0.96) in predicting the degradation of tensile strength, interlaminar shear strength, and flexural strength of FRPs exposed to hygrothermal ageing.
