Simplified model for the non-linear behaviour representation of reinforced concrete columns under biaxial bending

In the present paper a simplified model is proposed for the force-deformation behaviour of reinforced concrete members under biaxial loading combined with axial force. The starting point for the model development was an existing fixed-length plastic hinge element model that accounts for the non-linear hysteretic behaviour at the element end-sections, characterized by trilinear moment-curvature laws. To take into account the section biaxial behaviour, the existing model was adopted for both orthogonal lateral directions and an interaction function was introduced to couple the hysteretic response of both directions. To calibrate the interaction function it were used numerical results, obtained from fibre models, and experimental results. For the parameters identification, non-linear optimization approaches were adopted, namely: the gradient based methods followed by the genetic, evolutionary and nature-inspired algorithms. Finally, the simplified non-linear model proposed is validated through the analytical simulation of biaxial test results carried out in full-scale reinforced concrete columns

On the comparison of numerical methodologies for control optimisation of variable-speed pumps

No abstract available.publishe

Using different strategies for improving efficiency in water supply systems

Nowadays, the major expenses with water supply systems (WSS) correspond to energy consumption. In WSS, the pumps are only activated when the reservoirs, responsible for supplying certain populations, reach their minimum levels. The introduction of a pump pattern adapted to energy prices variation and consumption patterns of populations can minimize energy costs significantly. In this paper, two different examples of water supply networks simulations are introduced. For these WSS the application of different optimization methods that minimize the costs associated to energy consumption in water pumping are presented. The selected optimization methods were the method of Levenberg-Marquardt (LM) and an evolutionary algorithm. In both simulated examples, the optimization of pump pattern allows significant reductions in costs associated to the pumping (up to 71%). Reductions of energy consumption are also observed in one of the examples. It was found that the optimized pump patterns take into account the variation of the energy cost throughout the day. The classic method of LM proved to be the most efficient in this kind of optimization problems. The sequential use of both methods allows further reductions with the drawback of large CPU time.publishe

Analysis of diverse optimisation algorithms for pump scheduling in water supply systems

Nowadays, the major expenses with water supply systems (WSS) correspond to energy consumption. The number of scientific works dealing with operational optimisation in WSS has been increasing over the past years, demonstrating significant reductions on energy costs and consumption. Pump stations usually represent the major portion of total energy costs in WSS. Consequently, in this work, it is pretended to give a contribution for energy efficiency improvement in pump stations. Generally, in WSS, the pumps are switched on when the reservoirs, responsible for supplying certain populations, reach their minimum levels. These pumps are only switched off when the reservoirs reach their maximum levels. The introduction of an operational pump pattern adapted to the energy tariff variation and the consumption patterns of the populations can optimise pump stations operations, minimising energy costs significantly. However, the process of finding the best pattern can present difficulties due to the complexity of some WSS (multiple pumps, multiple reservoirs, nonlinear behaviour of the systems, etc). In this work, an interface was developed with the aim of applying different optimisation algorithms for pump scheduling in WSS. The interface makes an automatic connection between a hydraulic simulator (EPANET 2.0) and the different optimisation algorithms selected, providing, after multiple iterations and evaluations, an optimal pump pattern for a certain water supply network represented. Two different examples of water supply networks are introduced in this study in order to validate the developed methodology. For both WSS, classic and meta-heuristic optimisation algorithms are tested and analysed.publishe

Improving water supply systems efficiency using optimisation techniques

The energy costs due to pumps operation in Water Supply Systems (WSSs) constitute a large quota of the global costs. Pumps control optimisation can provide considerable improvements in WSSs efficiency since most of the times their operation reveals to be completely inefficient. A numerical methodology to optimise both the rotational speed and the operating time of variable-speed pumps is proposed. For the automatic application of such methodology in distinct networks, a computational tool combining EPANET 2.0 with an optimisation module was developed using C++ language. In the presented case-study, the use of a Particle Swarm Optimisation (PSO) algorithm with the proposed methodology allowed to reduce significantly the energy costs associated to water pumping.publishe

Short-term water demand forecasting using machine learning techniques

Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Shortterm water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.publishe

Modelling the Portevin-Le Chatelier effects in aluminium alloys

Plastic deformation processes are among the most demanding processes in manufacturing causing different microstructure feature in materials. A number of various dislocation patterns can be induced by plastic strain under different conditions. A serrated yielding/jerky flow in some dilute alloys like aluminium-magnesium alloys during plastic deformation is a well-known phenomenon under certain regimes of strain rate and temperature reported in a significant number of works. The serrated features in these materials are so-called the Portevin-Le Chatelier effects. The occurrence of these undesirable effects is due to the interaction between solute atoms and mobile dislocation during the plastic deformation which is known as dynamic strain ageing. There are a significant number of theoretical and numerical investigations that have been focused on describing the serrated behaviour of these materials during plastic deformation. Hence, the fundamental objective of this paper is a general review of different constitutive modelling in regards with this feature. The typical material models and new constitutive models describing this feature are presented. In addition, applications of the models are provided indicating their advantages and disadvantages

Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy

Recent research on water demand short-term forecasting has shown that models using univariate time series based on historical data are useful and can be combined with other prediction methods to reduce errors. The behavior of water demands in drinking water distribution networks focuses on their repetitive nature and, under meteorological conditions and similar consumers, allows the development of a heuristic forecast model that, in turn, combined with other autoregressive models, can provide reliable forecasts. In this study, a parallel adaptive weighting strategy of water consumption forecast for the next 24–48 h, using univariate time series of potable water consumption, is proposed. Two Portuguese potable water distribution networks are used as case studies where the only input data are the consumption of water and the national calendar. For the development of the strategy, the Autoregressive Integrated Moving Average (ARIMA) method and a short-term forecast heuristic algorithm are used. Simulations with the model showed that, when using a parallel adaptive weighting strategy, the prediction error can be reduced by 15.96% and the average error by 9.20%. This reduction is important in the control and management of water supply systems. The proposed methodology can be extended to other forecast methods, especially when it comes to the availability of multiple forecast models.by UE/FEDER through the program COMPETE 2020 and UID/EMS/00481/2013-FCT under CENTRO-01-0145-FEDER- 022083publishe

Inverse analysis procedures for elastoplastic parameter identification using combined optimisation strategies

Ensuring accurate and efficient models for the representation of the elastoplastic behaviour of sheet metals is one of the main issues in manufacturing simulation processes. Nowadays, there are a few solid numerical methodologies for predicting the material parameters from full-field strain measurements using digital image correlation (DIC) techniques. External methods, such as the Finite Element Model Updating (FEMU), search for the parameter set that minimises the gap between the experimental and numerical observations. In these methods, a total separation between the experimental and the numerical data occurs. Equilibrium methods, such as the Virtual Fields Method (VFM), search for the parameter set that balances the internal and external work according to the principle of virtual work, where the internal work is calculated using the constitutive model applied to the experimental strain field [1-5]. Both described methods are still expensive and non-robust, which is closely related with the adopted single-stage optimisation strategies. Such optimisation strategies can undergo problems of initial solution’s dependence, non-uniqueness of solution, local and premature convergence, physical constraints violation, etc. Therefore, the choice of an optimisation algorithm is not straightforward. The aim of this work is to implement and analyse advanced optimisation strategies with sequential, parallel and hybrid approaches in a parameter identification problem using both the VFM and the FEMU methods. The performance of a gradient least-squares (GLS) optimisation algorithm, a metaheuristic (MH) algorithm and their combination is compared. Moreover, the definition of the objective functions of both VFM and FEMU methods is discussed in the framework of optimisation.publishe