Abstract

The paper focuses on the analysis of radial-gated spillways, which is carried out by the solution of a numerical model based on the finite element method (FEM). The Oliana Dam is considered as a case study and the discharge capacity is predicted both by the application of a level-set-based free-surface solver and by the use of traditional empirical formulations. The results of the analysis are then used for training an artificial neural network to allow real-time predictions of the discharge in any situation of energy head and gate opening within the operation range of the reservoir. The comparison of the results obtained with the different methods shows that numerical models such as the FEM can be useful as a predictive tool for the analysis of the hydraulic performance of radial-gated spillways.

Abstract

Distributed optical fiber sensors (DOFS) are gaining momentum for in-situ condition monitoring and damage detection purposes. Although DOFS are a versatile sensing method enabling high-resolution strain and temperature mapping, they are also sensitive to mechanical vibrations. Vibrations are typically created by the ambient environment (e.g acoustic background, rotating equipment) which can produce high levels of measurement noise. With physical access to DOFS installations, the principle of acoustic or mechanical vibrations can also be utilized for malicious sensor tampering. The current lack of anomaly-detection systems suggests that practical DOFS applications would benefit from an automated analysis to detect and classify compromised measurements. Noise classification makes it possible to identify its source and potentially remove its effects from the measurement in the future. This would expand the commercial applications of DOFS systems significantly. Neural networks have been used for error detection in cyber-physical applications in numerous studies with high-accuracy results. Specifically, long short-term memory (LSTM) neural network models have become popular in recent years to classify anomalies in sequential e.g time-series data. Our investigation conducted a series of physical experiments using magnitude-controlled mechanical disturbances on bare free-hanging DOFS. Both random low-frequency vibrations at large displacement amplitudes and a constant high-frequency acoustic source at a low amplitude were employed. Experiments revealed that strain patterns are visually different with varying types and levels of disturbances. For the numerical analysis, statistics and machine learningbased approaches were applied for DOFS vibration noise classification, and their accuracy is discussed in detail. Results from the post-processing of compromised DOFS data suggest that it is possible to develop a vibration detection or classification system based on off-the-shelf DOFS interrogation equipment coupled with LSTM numerical tools.

Abstract

The mechanical and thermal behaviours of layered structures is of great importance for many advanced material systems and loading conditions. The responses of layered structures are controlled by the constitutive properties of each layer as well as the thicknesses. A comprehensive data-based approach is essential for both material analysis and design in direct or inverse problems. In this work parametric numerical modelling and Artificial Neural Network (ANN) are jointly used to develop data for layered structures. Mechanical and thermal finite element (FE) models are used to produce data for different material property and thickness domains. The use of ANN program is established and evaluated for different loading conditions. Using indentation as a typical case for mechanical loading and localized heating as typical example for thermal loading, ANN program was used to predict the behaviour of layered structures with different properties and layer thicknesses. Use of the data system in establishing dominating factors, synergetic effect on mechanical-thermal performance in advanced materials design is discussed.