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Computational fluid dynamics (CFD) studies are carried out on a two-bladed vertical axis wind turbine operating at a wind speed of 8 m/s for tip speed ratios (λ) of 0.50–3.0. The blade is the NACA0021 airfoil with chord length 0.265 m and rotor radius 1 m. Basic sensitivity studies for various time step sizes are carried out. The results are validated against available measurement data from the literature. Excellent agreement is obtained for small λ up to optimum condition. For the higher tip speed ratios, the two-dimensional CFD computations predict higher results than the wind tunnel experiment, but they are very similar to the field measurement data. Wake characteristics are presented, showing that the wake becomes Gaussian at 5 times radius downstream of the rotor. It is shown that complex flow phenomena occur owing to dynamic stall onset, especially for the smaller tip speed ratio.

This work presents an innovative control architecture, which takes its ideas from the theory of adaptive control techniques and the theory of statistical learning at the same time. Taking inspiration from the architecture of a classical neural network with several hidden levels, the principle is to divide the architecture of the adaptive controller into three different levels. Each level implements an algorithm based on learning from data and therefore we can talk about learning concepts. Each level has a different task: the first to learn the required reference to the control loop; the second to learn the coefficients of the state representation of a model of the system to be controlled; and finally, the third to learn the coefficients of the state representation of the actual controller. The design of the control system is reported from both a rigorous and an operational point of view. As an application example, the proposed control technique is applied on a second-order non-linear system. We consider a servo-drive based on a brushless DC (BLDC) motor, whose dynamic model considers all the non-linear effects related to the electromechanical nature of the electric machine itself, and also an accurate model of the switching power converter. The reported example shows the capability of the control algorithm to ensure trajectory tracking while allowing for disturbance rejection with different disturbance signal amplitude. The implementation complexity analysis of the new controller is also proposed, showing its low overhead vs. basic control solutions.

Efficiency of energy resource use is a key factor for a sustainable energy future. By matching the exergy levels of supply and demand, Energy Quality Management (EQM) of building energy supply systems may achieve more efficient use of energy resources. Beyond this, environmental impact of energy supply systems is another essential issue. This work addresses the influence of EQM on CO 2 emissions in the operation optimization of a Distributed Energy System (DES). A multi-objective linear programming problem is formulated to reduce energy costs and increase the overall exergy efficiency. Total CO 2 emissions are evaluated for the optimized operation strategies of the DES. The operators of the DES can choose the operation strategy from the Pareto front, based on their priorities and also aware of effects on CO 2 emissions. Results demonstrate more efficient use of energy resources and reduction in CO 2 emissions through EQM, as compared with conventional energy supply systems.

Alkylated furan derivatives, such as 2,5-dimethylfuran (2,5-DMF) and 2-methylfuran (2-MF), have shown, at laboratory scale, a relatively high tendency to form soot. However, soot emissions from die...

This paper presents a tradeoff analysis in terms of accuracy and computational cost between different architectures of artificial neural networks for the State of Charge (SOC) estimation of lithium batteries in hybrid and electric vehicles. The considered layouts are partly selected from the literature on SOC estimation, and partly are novel proposals that have been demonstrated to be effective in executing estimation tasks in other engineering fields. One of the architectures, the Nonlinear Autoregressive Neural Network with Exogenous Input (NARX), is presented with an unconventional layout that exploits a preliminary routine, which allows setting of the feedback initial value to avoid estimation divergence. The presented solutions are compared in terms of estimation accuracy, duration of the training process, robustness to the noise in the current measurement, and to the inaccuracy on the initial estimation. Moreover, the algorithms are implemented on an electronic control unit in serial communication with a computer, which emulates a real vehicle, so as to compare their computational costs. The proposed unconventional NARX architecture outperforms the other solutions. The battery pack that is used to design and test the networks is a 20 kW pack for a mild hybrid electric vehicle, whilst the adopted training, validation and test datasets are obtained from the driving cycles of a real car and from standard profiles.

Exterior insulation finishing systems (EIFSs) can efficiently promote energy efficiency of buildings. In this study, an EIFS with high thermal efficiency is presented to improve the insulation behavior of building enclosure. Based on heat transfer analysis results, energy simulations of buildings with fire spread prevention structures were performed. Results revealed that heat flow through the wall increased by 10.3 % when using a metal rail to fix the insulation; in contrast, using non-combustible phenolic foam reduces heat flow by 37.4 %, satisfying the requirement for fire spread prevention structures. Additionally, the energy consumption decreased by 8.8 % when both mineral wool and phenolic foam were applied. Fire spread prevention structures are essential to improve the fire safety performance of buildings. This external insulation system efficiently promote energy saving in building; additionally, leveraging a phase change material to improve the thermal storage performance of the building can reduce energy consumption by up to 11.9 %.

This paper investigates the impact of the standard approximations embodied in the well-known Momentum Theory on its performance prediction capabilities. To this aim, the results of the momentum theory, which is still widely used in all Blade Element/Momentum codes for the analysis and/or design of wind turbines, are compared with those obtained with an actuator disk model based on Computational Fluid Dynamics techniques. In this method, the axisymmetric and steady Euler equations are solved with a classical finite volume approach, while the turbine effects are modelled through a set of axial and tangential body forces distributed over a disk shaped region representing the rotor swept surface. Since this method does not rely on the momentum theory simplifying assumptions, it can be suitably employed to verify the momentum theory validity. The analysis is carried out using the well documented experimental data of the National Renewable Energy Laboratory Phase VI wind turbine.