On cost-effective reuse of components in the design of complex reconfigurable systems

Design strategies that benefit from the reuse of system components can reduce costs while maintaining or increasing dependability—we use the term dependability to tie together reliability and availability. D3H2 (aDaptive Dependable Design for systems with Homogeneous and Heterogeneous redundancies) is a methodology that supports the design of complex systems with a focus on reconfiguration and component reuse. D3H2 systematizes the identification of heterogeneous redundancies and optimizes the design of fault detection and reconfiguration mechanisms, by enabling the analysis of design alternatives with respect to dependability and cost. In this paper, we extend D3H2 for application to repairable systems. The method is extended with analysis capabilities allowing dependability assessment of complex reconfigurable systems. Analysed scenarios include time-dependencies between failure events and the corresponding reconfiguration actions. We demonstrate how D3H2 can support decisions about fault detection and reconfiguration that seek to improve dependability while reducing costs via application to a realistic railway case study

Nonlinear Optical Response of a Plasmonic Nanoantenna to Circularly Polarized Light: Rotation of Multipolar Charge Density and Near-Field Spin Angular Momentum Inversion

The spin and orbital angular momentum carried by electromagnetic pulses open new perspectives to control nonlinear processes in light–matter interactions, with a wealth of potential applications. In this work, we use time-dependent density functional theory (TDDFT) to study the nonlinear optical response of a free-electron plasmonic nanowire to an intense, circularly polarized electromagnetic pulse. In contrast to the well-studied case of the linear polarization, we find that the nth harmonic optical response to circularly polarized light is determined by the multipole moment of order n of the induced nonlinear charge density that rotates around the nanowire axis at the fundamental frequency. As a consequence, the frequency conversion in the far field is suppressed, whereas electric near fields at all harmonic frequencies are induced in the proximity of the nanowire surface. These near fields are circularly polarized with handedness opposite to that of the incident pulse, thus producing an inversion of the spin angular momentum. An analytical approach based on general symmetry constraints nicely explains our numerical findings and allows for generalization of the TDDFT results. This work thus offers new insights into nonlinear optical processes in nanoscale plasmonic nanostructures that allow for the manipulation of the angular momentum of light at harmonic frequencies.We acknowledge financial support from project IT1526–22 of the Department of Education of the Basque Government, and projects PID2019–107432GB-I00 and PID2022–139579NB-I00, funded by MCIN/AEI/10.13039/501100011033 and “FEDER Una manera de hacer Europa”

Dispersive surface-response formalism to address nonlocality in extreme plasmonic field confinement

The surface-response formalism (SRF), where quantum surface-response corrections are incorporated into the classical electromagnetic theory via the Feibelman parameters, serves to address quantum effects in the optical response of metallic nanostructures. So far, the Feibelman parameters have been typically obtained from many-body calculations performed in the long-wavelength approximation, which neglects the nonlocality of the optical response in the direction parallel to the metal–dielectric interface, thus preventing to address the optical response of systems with extreme field confinement. To improve this approach, we introduce a dispersive SRF based on a general Feibelman parameter d ⊥(ω, k ‖), which is a function of both the excitation frequency, ω, and the wavenumber parallel to the planar metal surface, k ‖. An explicit comparison with time-dependent density functional theory (TDDFT) results shows that the dispersive SRF correctly describes the plasmonic response of planar and nonplanar systems featuring extreme field confinement. This work thus significantly extends the applicability range of the SRF, contributing to the development of computationally efficient semiclassical descriptions of light–matter interaction that capture quantum effects.MCIN/AEI/10.13039/501100011033/ (PID2019-107432GB-I00); Department of Education of the Basque Government (IT1526-22); “Investissements d’Avenir” LabEx PALM (ANR-10-LABX-0039-PALM)

Optoelectronics of subnanometric metallic gaps

Trabajo presentado al 44th Winter Colloquium on the Physics of Quantum Electronics, celebrado en Utah (USA) del 5 al 9 de enero de 2014.Peer reviewe

Bridging quantum and classical plasmonics with a quantum-corrected model

Electromagnetic coupling between plasmonic resonances in metallic nanoparticles allows for engineering of the optical response and generation of strong localized near-fields. Classical electrodynamics fails to describe this coupling across sub-nanometer gaps, where quantum effects become important owing to non-local screening and the spill-out of electrons. However, full quantum simulations are not presently feasible for realistically sized systems. Here we present a novel approach, the quantum-corrected model (QCM), that incorporates quantum-mechanical effects within a classical electrodynamic framework. The QCM approach models the junction between adjacent nanoparticles by means of a local dielectric response that includes electron tunnelling and tunnelling resistivity at the gap and can be integrated within a classical electrodynamical description of large and complex structures. The QCM predicts optical properties in excellent agreement with fully quantum mechanical calculations for small interacting systems, opening a new venue for addressing quantum effects in realistic plasmonic systems

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Prognostics and health management oriented data analytics suite for transformer health monitoring

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety, and economic effects. With the advance of machine learning techniques, it is possible to enhance traditional transformer health monitoring techniques with data-driven and expert-based prognostics and health management (PHM) applications. Accordingly, this paper reviews the experience of the authors in the implementation of machine learning methods for transformer condition monitoring

Uncertainty analysis of two gas measurement DGA ratios for improved diagnostics applications

This paper formulates the exact analytical probability density function (PDF) for the ratio of two independent dissolved gas analysis (DGA) measurements that include individual gas measurement errors. It is demonstrated that for small DGA gas measurement errors, the correct two-gas ratio PDF approaches a conventional Gaussian distribution. As the measurement accuracy decreases, the ratio PDF becomes non-Gaussian with the maximum likelihood value of the PDF deviating from the true underlying value. For larger errors, the maximum likelihood estimate of the gas ratio deviates significantly from presumed Gaussian statistics. A method for de-biasing measured gas ratio values is presented and a simple application is used to demonstrate the proposed approach

Enhanced fano resonance of organic material films deposited on arrays of asymmetric split-ring resonators (A-SRRs)

Depositing very thin organic films on the surface of arrays of asymmetric split-ring resonators (A-SRRs) produces a shift in their resonance spectra that can be utilized for sensitive analyte detection. Here we show that when poly-methyl-methacrylate (PMMA) is used as an organic probe (analyte) on top of the A-SRR array, the phase and amplitude of a characteristic molecular Fano resonance associated with a carbonyl bond changes according to the spectral positions of the trapped mode resonance of the A-SRRs and their plasmonic reflection peaks. Furthermore, we localize blocks of PMMA at different locations on the A-SRR array to determine the effectiveness of detection of very small amounts of non-uniformly distributed analyte