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The purpose of the paper is to predict the aerodynamic performance of a complete scale model H-Darrieus vertical axis wind turbine (VAWT) with end plates at different operating conditions. This paper aims at understanding the flow physics around a model VAWT for three different tip speed ratios corresponding to three different flow regimes.

This study achieves a first three-dimensional hybrid lattice Boltzmann method/very large eddy simulation (LBM-VLES) model for a complete scaled model VAWT with end plates and mast using the solver PowerFLOW. The power curve predicted from the numerical simulations is compared with the experimental data collected at Erlangen University. This study highlights the complexity of the turbulent flow features that are seen at three different operational regimes of the turbine using instantaneous flow structures, mean velocity, pressure iso-contours, blade loading and skin friction plots.

The power curve predicted using the LBM-VLES approach and setup provides a good overall match with the experimental power curve, with the peak and drop after the operational point being captured. Variable turbulent flow structures are seen over the azimuthal revolution that depends on the tip speed ratio (TSR). Significant dynamic stall structures are seen in the upwind phase and at the end of the downwind phase of rotation in the deep stall regime. Strong blade wake interactions and turbulent flow structures are seen inside the rotor at higher TSRs.

The computational cost and time for such high-fidelity simulations using the LBM-VLES remains expensive. Each simulation requires around a week using supercomputing facilities. Further studies need to be performed to improve analytical VAWT models using inputs/calibration from high fidelity simulation databases. As a future work, the impact of turbulent and nonuniform inflow conditions that are more representative of a typical urban environment also needs to be investigated.

The LBM methodology is shown to be a reliable approach for VAWT power prediction. Dynamic stall and blade wake interactions reduce the aerodynamic performance of a VAWT. An ideal operation close to the peak of the power curve should be favored based on the local wind resource, as this point exhibits a smoother variation of forces improving operational performance. The 3D flow features also exhibit a significant wake asymmetry that could impact the optimal layout of VAWT clusters to increase their power density. The present work also highlights the importance of 3D simulations of the complete model including the support structures such as end plates and mast.

Accurate predictions of power performance for Darrieus VAWTs could help in better siting of wind turbines thus improving return of investment and reducing levelized cost of energy. It could promote the development of onsite electricity generation, especially for industrial sites/urban areas and renew interest for VAWT wind farms.

A first high-fidelity simulation of a complete VAWT with end plates and supporting structures has been performed using the LBM approach and compared with experimental data. The 3D flow physics has been analyzed at different operating regimes of the turbine. These physical insights and prediction capabilities of this approach could be useful for commercial VAWT manufacturers.

The paper presents a techno-economic analysis of the electromechanical equipment of traditional vertical axis water mills (VAWMs) to help investors, mill owners and engineers to preliminary estimate related benefits and costs of a VAWM repowering.

Two sustainable repowering solutions were examined with the additional aim to preserve the original status and aesthetics of a VAWM: the use of a vertical axis water wheel (VAWW) and a vertical axis impulse turbine. The analysis was applied to a database of 714 VAWMs in Basilicata (Italy), with known head and flow.

Expeditious equations were proposed for both solutions to determine: (1) a suitable diameter as a function of the flow rate; (2) the costs of the electromechanical equipment; (3) achievable power. The common operating hydraulic range of a VAWM (head and flow) was also identified. Reality checks on the obtained results are shown, in particular by examining two Spanish case studies and the available literature. The power generated by the impulse turbine (Turgo type) is twice that of a VAWW, but it is one order of magnitude more expensive. Therefore, the impulse turbine should be used for higher power requirements (>3 kW), or when the electricity is delivered to the grid, maximizing the long-term profit.

Since there is not enough evidence about the achievable performance and cost of a VAWM repowering, this work provides expeditious tools for their evaluation.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

Apart from the basic material properties of liquid lubricants, such as, e.g., the viscosity and density of the hydraulic fluid, it is also important to have information regarding the electrical properties of the fluid used. The latter is closely related to the purpose, type, structure, and conditions of use of a hydraulic system, especially the powertrain design and fluid condition monitoring. The insulating capacity of the hydraulic fluid is important in cases where the electric motor of the pump is immersed in the fluid. In other cases, on the basis of changing the electrical conductive properties of the hydraulic fluid, we can refer its condition, and, on this basis, the degree of degradation.

The paper first highlights the importance of knowing the electrical properties of hydraulic fluids and then aims to compare these properties, such as the breakdown voltage of commonly used hydraulic mineral oils and newer ionic fluids suitable for use as hydraulic fluids.

Knowledge of this property is crucial for the design approach of modern hydraulic compact power packs. In the following, the emphasis is on the more advanced use of known electrical quantities, such as electrical conductivity and the dielectric constant of a liquid.

Based on the changes in these quantities, we have the possibility of real-time monitoring the hydraulic fluid condition, on the basis of which we judge the degree of fluid degradation and its suitability for further use.