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Highlights • Identification of constitutive model parameters for elasto-plastic fracture. • Multiscale experimental testing of silver-plated copper busbars for photovoltaics. • Identification based on the gradient descent method and particle swarm optimization. Abstract Silver-plated copper busbars are screen printed onto silicon solar cells and have the key role to collect the electric current produced by the solar cell. Busbars of two adjacent solar cells are then connected by a soldered ribbon made of the same material. Due to mechanical and thermal loads, such a ribbon is subject to axial deformation that, often, causes plasticity and, in some cases, its breakage due to crack growth. A procedure based on the gradient-descent method and particle swarm optimization is herein proposed for the identification of elasto-plastic and nonlinear (cohesive zone model, CZM) fracture mechanics parameters of silver-plated copper busbars. The proposed method requires the experimental determination of the force-displacement curves from uniaxial tensile tests on busbar samples with and without initial notches. The inspection of in situ SEM images during the tests allows also the estimation of the crack opening, which is found to be an important local quantity to assess the reliability of different CZMs in simulating a crack growth process consistent with the real one. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a procedure for the evaluation of bounds on the parameters of Hammerstein systems, from output measurements affected by bounded errors. The identification problem is formulated in terms of polynomial optimization, and relaxation techniques, based on linear matrix inequalities, are proposed to evaluate parameter bounds by means of convex optimization. The structured sparsity of the formulated identification problem is exploited to reduce the computational complexity of the convex relaxed problem. Analysis of convergence properties and computational complexity is reported. [Copyright &y& Elsevier]

Information on residential power consumption patterns disaggregated at the single-appliance level is an essential requirement for energy utilities and managers to design customized energy demand management strategies. Non-Intrusive Load Monitoring (NILM) techniques provide this information by decomposing the aggregated electric load measured at the household level by a single-point smart meter into the individual contribution of each end-use. Despite being defined non-intrusive , NILM methods often require an intrusive data sampling process for training purpose. This calibration intrusiveness hampers NILM methods large-scale applications. Other NILM challenges are the limited accuracy in reproducing the end-use consumption patterns and their trajectories in time, which are key to characterize consumers’ behaviors and appliances efficiency, and the poor performance when multiple appliances are simultaneously operated. In this paper we contribute a hybrid, computationally efficient, algorithm for NILM, called Hybrid Signature-based Iterative Disaggregation (HSID), based on the combination of Factorial Hidden Markov Models, which provide an initial approximation of the end-use trajectories, and Iterative Subsequence Dynamic Time Warping, which processes the end-use trajectories in order to match the typical power consumption pattern of each appliance. In order to deal with the challenges posed by intrusive training, a supervised version of the algorithm, requiring appliance-level measurements for calibration, and a semi-supervised version, retrieving appliance-level information from the aggregate smart-metered signal, are proposed. Both versions are demonstrated onto a real-world power consumption dataset comprising five different appliances potentially operated simultaneously. Results show that HSID is able to accurately disaggregate the power consumption measured from a single-point smart meter, thus providing a detailed characterization of the consumers’ behavior in terms of power consumption. Numerical results also demonstrate that HSID is robust with respect to noisy signals and scalable to dataset including a large set of appliances. Finally, the algorithm can be successfully used in non-intrusive experiments without requiring appliance-level measurements, ultimately opening up new opportunities to foster the deployment of large-scale smart metering networks, as well as the design and practical implementation of personalized demand management strategies. [ABSTRACT FROM AUTHOR]

Over the last two decades, water smart metering programs have been launched in a number of medium to large cities worldwide to nearly continuously monitor water consumption at the single household level. The availability of data at such very high spatial and temporal resolution advanced the ability in characterizing, modeling, and, ultimately, designing user-oriented residential water demand management strategies. Research to date has been focusing on one or more of these aspects but with limited integration between the specialized methodologies developed so far. This manuscript is the first comprehensive review of the literature in this quickly evolving water research domain. The paper contributes a general framework for the classification of residential water demand modeling studies, which allows revising consolidated approaches, describing emerging trends, and identifying potential future developments. In particular, the future challenges posed by growing population demands, constrained sources of water supply and climate change impacts are expected to require more and more integrated procedures for effectively supporting residential water demand modeling and management in several countries across the world. [ABSTRACT FROM AUTHOR]

Abstract Impacted by a complex mixture of urban, industrial, shipping and also natural emissions, Marseille, the second most populated city in France, represents a very interesting case study for the apportionment of PM 2.5 sources in a Mediterranean urban environment. In this study, daily PM 2.5 samples were collected over a one-year period (2011−2012) at an urban background site, and were comprehensively analyzed for the determination of organic carbon (OC), elemental carbon (EC), major ions, trace elements/metals and specific organic markers. A constrained positive matrix factorization (PMF) analysis using the ME-2 (multilinear engine-2) solver was applied to this dataset. PMF results highlighted the presence of two distinct fingerprints for biomass burning (BB1 and BB2). BB1, assigned to open green waste burning peaks in fall (33%; 7.4 μg m−3) during land clearing periods, is characterized by a higher levoglucosan/OC ratio, while BB2, assigned to residential heating, shows the highest contribution during the cold period in winter (14%; 3.3 μg m−3) and it is characterized by high proportions from lignin pyrolysis products from the combustion of hardwood. Another interesting feature lies in the separation of two fossil fuel combustion processes (FF1 and FF2): FF1 likely dominated by traffic emissions, while FF2 likely linked with the harbor/industrial activities. On annual average, the major contributors to PM 2.5 mass correspond to the ammonium sulfate-rich aerosol (AS-rich, 30%) and to the biomass burning emissions (BB1 + BB2, 23%). This study also outlined that during high PM pollution episodes (PM 2.5 > 25 μg m−3), the largest contributing sources to PM 2.5 were biomass burning (33%) and FF1 (23%). Moreover, 28% of the ambient mass concentration of OC is apportioned by the AS-rich factor, which is representative of an aged secondary aerosol, reflecting thus the importance of the oxidative processes occurring in a Mediterranean environment. Graphical abstract Unlabelled Image Highlights • A constrained PMF analysis (ME2) successfully applied with a large array of organic and inorganic markers. • Two distinct fingerprints for biomass burning resolved. • Two signatures for the fossil fuel combustion processes separated. • Secondary processes and biomass burning emissions are the major contributors to PM 2.5. [ABSTRACT FROM AUTHOR]