• Energy Research
• 2021-2025close
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• Polytechnic University of Milan close

Electrolyte imbalance caused by the undesired vanadium-ions cross-over and water transport through the membrane is one of the main critical issues of vanadium redox flow batteries, leading to battery capacity loss and electrolytes volume variation. In this work, the evolution of discharged capacity and electrolyte volume variation were firstly investigated adopting commercial electrolyte for hundreds of charge-discharge cycles in vanadium redox flow batteries employing different membranes, varying thickness and equivalent weight. Subsequently, with the support of a 1D physics-based model, the origin of the main phenomena regulating capacity decay and volume variation has been identified and different modifications in the preparation of electrolytes have been proposed. Electrolytes characterized by an equal proton concentration between the two tanks at the beginning of cycling operation turned out to limit capacity decay, while increasing electrolyte proton concentration was effective also in the mitigation of volume variation. The most promising electrolyte preparation combined the effect of high proton concentration and null osmotic pressure gradient between the two tanks: compared to commercial electrolyte this preparation reduced the capacity decay from 47.7% to 20.9%, increased the coulombic efficiency from 96.2% to 98.9% and the energy one from 79.9% to 83.4%, and also implied a negligible volume variation during cycles. The effectiveness of this electrolyte preparation has been verified with different membranes, increasing the range of validity of the results, that could be thus applied in a real system regardless of the adopted membrane.

Abstract With its scientifically proven effects and widespread acceptance, climate change stands as one of the most pressing and intricate challenges for society and economies. Farmers are on the frontline of managing climate change. Therefore, how they perceive and respond to climate change shapes their risk assessment and structures future resilience and adaptive capacity. Employing a bottom-up approach, we conducted 460 surveys randomly among farmers throughout the Lombardy region in northern Italy. A triple-loop approach considering climate change awareness, perceived impacts, and adaptation measures and barriers was implemented to characterize the potential heterogeneity of farmer behaviour and explore whether risk attitudes and adaptation actions differ between farmer profiles. We then profiled farmers through a clustering analysis. Four groups emerged, highlighting farmers’ adaptation preferences and risk attitudes: (1) cropping-adapted and isolated farmers, (2) cooperation-adapted and insecure farmers, (3) risk insurance-adapted and confident farmers, and (4) climate services-adapted and aware farmers. Although the groups vary in terms of farmers’ characterization and farming activities (e.g. young and highly educated vs. old and highly experienced farmers, rainfed vs. irrigated farms), similar patterns were observed regarding climate change awareness and perceived impacts—however, the contrast increased in terms of adaptation measures and barriers. Gaining a more comprehensive understanding of the diverse ways in which farmers assess risks and adapt can promote the transferability of bottom-up findings and inform the co-design of tailored and flexible adaptation instruments, minimizing the risk of maladaptation or ineffective transformation in the face of climate change.

AbstractThis article describes a control approach for large‐scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118‐bus system is used to test the proposed design procedure in a nontrivial scenario.

The Sustainable Development Goals (SDGs) of the United Nations state that cities and human settlements need to be more inclusive, safe and resilient. In Europe cities have experienced dramatic physical, social and economic changes during the last decades while historic centres of European cities, among the most important assets of the European cultural heritage, are living paradoxes. They are defined as “a collection of beauty, icon of well-being, model of sustainability, but abandoned”. This study investigates the changes in the urban landscape of Nicosia, a particular historical centre in the Mediterranean region (Cyprus). The city centre is characterised by exceptionally well-preserved Venetian fortifications. Due to political circumstances, the capital of Cyprus, Nicosia, is still divided and has been ruled by two different administrations for several decades. This study used optical multi-spectral satellite datasets processing, like the Landsat and the most recent Sentinel-2 products, to detect, identify and characterise significant morphological transformations within the walled city and around it. This paper’s central thesis promotes a more systematic use of earth observation products and derivatives in decision-making processes that regard planning, use and management of urban resources in Europe, especially in support of urban planning strategies of historic cities.

As it emerges from the literature, electricity access in rural contexts is deeply intertwined with socioeconomic dynamics. However, the advent of a reliable and sufficient source of electricity is not the sole driver that might contribute to local development. Indeed, complementary activities might have a crucial role in sustaining the development of rural communities as well as the electricity access. The current research addresses the lack of counterfactual scenarios in which the impact of complementary activities on electrification projects can be investigated. The authors introduce the case study of Matembwe village, a rural community in the Njombe region of Tanzania. The data collection includes the electricity consumption, number of electricity connections, and number of income-generating activities in a timespan ranging from 1989 to 2015. The analysis is based on system dynamics. The study considers different scenarios representing the dynamics related to the following complementary actions: access to market measures, access to credit measures, and access to usable skills. On the one hand, the study reveals that the effectiveness of the considered complementary actions is limited except from the access to microcredit which fosters an increase in electricity connections by 17%. On the other hand, both access to microcredit and the starting up of a local cooperative by CEFA Onlus that reinvests its profits in the local market impact the socio-economic dimension by 69% and 22%, respectively.

Abstract Fundamental studies on Flame-Wall Interaction (FWI) are of the utmost importance to unravel the intricate coupling between chemistry and transport in the near-wall region, and to characterize the quenching dynamics. For this purpose, an accurate description of reaction kinetics is especially needed. In this work, the role of chemistry during the wall quenching of premixed dimethyl ether (DME) flames is numerically investigated, leveraging recently-obtained experimental data in a Side-Wall Quenching configuration (SWQ) in a variety of conditions. A detailed kinetic model describing the low-to-high temperature oxidation of DME is used as starting point. In order to accommodate it into 2D simulations, it is first reduced to a skeletal level, both accounting for (31 species and 140 reactions) and neglecting (20 species and 93 reactions) low-temperature chemistry, and successfully validated against the original model in predicting flame propagation and Head-On Quenching (HOQ). Using an established CFD framework, the SWQ features of DME are investigated in terms of flame structure, heat fluxes and thermochemical CO-T states. A good agreement with the available experimental data is observed in the operating space. In the near-wall region, the prediction of the thermochemical state is most critical, and it is found that diffusion is here the prevailing contribution in defining such state. In the same region, kinetic analysis shows that low-temperature chemistry affects the speciation of the quenching region to a good extent, which is modified with respect to a freely-propagating flame. Yet, due to the lower reactivity, SWQ thermal features are not significantly influenced. As a result, for the purposes of the wall quenching analysis, only high-temperature chemistry needs to be accounted for, and the 20-species mechanism can be safely considered as a consolidated milestone for future studies.

Abstract Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method.