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The behaviours of individual flow structures have become a relevant matter of study in turbulent flows as the computational power to allow their study feasible has become available. Especially, high instantaneous Reynolds Stress events have been found to dominate the behaviour of the logarithmic layer. In this work, we present a viability study where two machine learning solutions are proposed to reduce the computational cost of tracking such structures in large domains. The first one is a Multi-Layer Perceptron. The second one uses Long Short-Term Memory (LSTM). Both of the methods are developed with the objective of taking the the structures’ geometrical features as inputs from which to predict the structures’ geometrical features in future time steps. Some of the tested Multi-Layer Perceptron architectures proved to perform better and achieve higher accuracy than the LSTM architectures tested, providing lower errors on the predictions and achieving higher accuracy in relating the structures in the consecutive time steps.

This paper presents an economic assessment of three drying systems (DS) with different configurations: diesel-powered (DDS), grid-powered (GDS), and photovoltaic-powered (PVDS). The third configuration, PVDS, presents important technological innovations regarding the electric configuration (stand-alone PV, without batteries or grid support) and the heat pump technology used (optimized for drying the recirculated air, which permits a reduction in the electricity consumption by more than 45% if compared to a diesel air-heater or an electric resistance). The profitability of the investment required for the installation of the three systems is proven by calculating the profitability index (PI), the internal rate of return (IRR), the payback period (PBP), and the levelized cost of energy (LCOE). All the indicators show good profitabilities for the three technologies (the PI values range from 6.78 to 10.11, the IRR values range from 24% to 37%, and the PBPs are between 3 and 4 years), but the PVDS clearly offers important economic advantages: its PI and IRR are more than 40% higher, the PBP is 25% shorter, and the LCOE is between 40 and 47% lower than for the two other systems. The results of this analysis correspond to a particular base case but are obviously sensitive to geopolitical and economic changes: the ultimate strength of the PVDS configuration is its resilience against a possible rise in electricity and diesel prices, reducing the uncertainty for investors and policymakers.

Variable refrigerant flow (VRF) systems are one possible tool to meet the objective that all new buildings must be nearly zero-energy buildings by 31 December 2020. Building Information Modelling (BIM) is a methodology that centralizes building construction project information in a digital model promoting collaboration between all its agents. The objectives of this work were to develop a more precise model of the VRF system than the one available in EnergyPlus version 8.9 (US Department of Energy) and to study the operation of this system in an office building under different climates by implementing the building energy simulation in an Open BIM workflow. The percentage deviation between the estimation of the VRF energy consumption with the standard and the new model was 6.91% and 1.59% for cooling and heating respectively in the case of Barcelona and 3.27% and 0.97% respectively in the case of Madrid. The energy performance class of the analysed building was A for each climatic zone. The primary energy consumption of the office building equipped with the VRF system was of 65.8 kWh/(m2·y) for the Mediterranean climate of Barcelona and 72.4 kWh/(m2·y) for the Continental climate of Madrid.

The present work studies biofuel production using an American native species that belongs to the Argemone genus. It is considered a weed, and its presence extends from the southern United States to some areas of South America; the species Argemone pleiacantha, together with other species of the same genus, is known as “chicalote”. Oil physical and chemical properties confirm that chicalote oil is an effective raw material for biofuel production, presenting a fatty acid composition similar to that of soybean oil. A biodiesel production study was carried out using two methods of synthesis, conventional and ultrasound-assisted transesterification, employing the same molar ratio and amount of catalyst in both cases. Reaction time and supplied energy during synthesis were compared in batch mode. The results revealed that ultrasound-assisted transesterification has significant advantages over the conventional one in terms of reaction time and energy savings during chicalote oil synthesis to produce fatty acid methyl esters.

The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V), lithium polymer battery (30 Ah, 37 V), and a supercapacitor (167 F, 48 V).

Climate change, clean energy transition, the energy security quest, and international relations have triggered the revival of renewable energy as a solution to these problems. Nowadays, there is an energy transition where renewable energies bring geopolitical changes in a world where fossil fuels are becoming less relevant. This article aims to assess how the transition influences Spain’s energy relations with other countries regarding electricity and its sources, in alignment with the European Green Deal. In order to do so, its current energy situation, the renewable energies development and its energy import-export relations are examined. The results show that despite progress in green regionalization through more electric interconnection, little difference is to be found in traditional relations with fossil fuel countries exporters, but more are the contractions in Spanish energy economic policy, as here is explained.