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Mixing of counterpropagating signals in a traveling-wave Josephson device
No full textInternational audienceLight waves do not interact in vacuum, but may mix through various parametric processes when traveling in a nonlinear medium. In particular, a high-amplitude wave can be leveraged to frequency convert a low-amplitude signal, as long as the overall energy and momentum of interacting photons are conserved. These conditions are typically met when all waves propagate in the medium with identical phase velocity along a particular axis. In this work, we investigate an alternative scheme by which an input microwave signal propagating along a 1-dimensional Josephson metamaterial is converted to an output wave propagating in the opposite direction. The interaction is mediated by a pump wave propagating at low phase velocity. In this novel regime, the input signal is exponentially attenuated as it travels down the device. We exploit this process to implement a robust on-chip microwave isolator that can be reconfigured into a reciprocal and tunable coupler. The device mode of operation is selected in situ, along with its working frequency over a wide microwave range. In the 5.5-8.5 GHz range, we measure an isolation over 15 dB on a typical bandwidth of 100 MHz, on par with the best existing on-chip isolators. Substantial margin for improvement exists through design optimization and by reducing fabrication disorder, opening new avenues for microwave routing and processing in superconducting circuits
Shape Optimization of Mechanical Specimens for Complex Material Model Identification Using CutFEM and Digital Image Correlation
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Pyrite morphology and sulfur isotopes refine taphonomic models for the 2.1 Ga Francevillian biota, Gabon
No full textInternational audiencePyritization is a key taphonomic process that preserves some of Earth's oldest fossils. It is influenced by various factors such as organic matter type, the availability of iron and sulfur, and sedimentation rates. In this study, we analyzed pyritized biotic and abiotic structures from 2.1 Ga deposits in Gabon's Francevillian Basin, to reconstruct their taphonomic pathway at the micron scale. Using secondary ion mass spectrometry and scanning electron microscopy, we examine sulfur isotope compositions, pyrite morphology and grain size within individual fossils and compare them to abiotic pyritic concretions from the same stratigraphic level. Our results reveal differences in pyrite grain size and sulfur isotope composition between fossils and concretions. More importantly, chemical and morphological variations are observed within individual fossils, likely due to distinct reactive environments for pyrite mineralization, linked to organic matter, sulfate and iron availability during early diagenesis. This remarkable variation in pyrite morphology and δ34S values in the fossilized specimens, indicates that they were compositionally more complex than the substrate that formed the homogeneously pyritized concretions. This well-preserved ecological window represents an exceptional record of the earliest multicellular life forms on Earth
Biomonitoring of Airborne Particulate Matter Using Plane Tree Bark: Method Development and First Insights into Oxidative Potential measurement
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Spontaneous stochasticity and the Armstrong-Vicol passive scalar
No full textSpontaneous stochasticity refers to the emergence of intrinsic randomness in deterministic systems under singular limits, a phenomenon conjectured to be fundamental in turbulence. Armstrong and Vicol [3, 4] recently constructed a deterministic, divergencefree multiscale vector field arbitrarily close to a weak Euler solution, proving that a passive scalar transported by this field exhibits anomalous dissipation and lacks a selection principle in the vanishing diffusivity limit.This work aims to explain why this passive scalar exhibits both Lagrangian and Eulerian spontaneous stochasticity.Part I provides a historical overview of spontaneous stochasticity, details the Armstrong-Vicol passive scalar model, and presents numerical evidence of anomalous diffusion, along with a refined description of the Lagrangian flow map.In Part II, we develop a theoretical framework for Eulerian spontaneous stochasticity. We define it mathematically, linking it to ill-posedness and finite-time trajectory splitting, and explore its measure-theoretic properties and the connection to RG formalism. This leads us to a well-defined measure selection principle in the inviscid limit. Within this limit, we identify a structured family of probability measures, with building blocks the Dirac measures responsible for the well-posed inviscid limits. This approach allows us to rigorously classify universality classes based on the ergodic properties of regularisations. To complement our analysis, we provide simple yet insightful numerical examples.Finally, we show that the absence of a selection principle in the Armstrong-Vicol model corresponds to Eulerian spontaneous stochasticity of the passive scalar. We also numerically compute the probability density of the effective renormalised diffusivity in the inviscid limit. This Eulerian behaviour contrasts sharply with the Kraichnan model, where only Lagrangian spontaneous stochasticity is possible. We argue that the lack of a selection principle should be understood as a measure selection principle over weak solutions of the inviscid system.</div
Effects of clouds, aerosols and shadows on solar energy potential on urban rooftops using earth observation data and digital surface models
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Design And Optimization Of A Heat Pump Coupled With A Storage System For District Heating
No full textInternational audienceThis paper presents a methodology for integrating a heat pump (HP) with a thermal energy storage (TES) system for district heating applications. The proposed system aims to increase energy efficiency, reduce operational costs, and improve the flexibility of the urban heating network. A linear programming (LP) model is developed to optimize the design and operation of the HP-TES system. The annual heating demand is simplified into four representative days to reduce computational complexity and facilitate the optimization process. The results demonstrate that integrating TES with a properly sized HP reduces both capital and operating expenses compared to a system without storage, making it a viable solution for sustainable urban heating
Unlocking engineering expertise frozen by grand challenges: dealing with transitions' unknown
No full textInternational audienceThe contemporary transition of climate change, technology evolution, and social inequality present profound challenges in project management. It requires multitude of stakeholders who are facing many unknowns on the solution to develop. This study explores how grand challenges impact the nature of unknowns encountered by engineering actors in project management. Through a historical analysis of innovation projects within a century-old automotive manufacturer, we identify distinct types of unknowns-desirable, undesirable, endogenous, and exogenous-and examine the processes required to manage them, including endogenization and desirabilization. We demonstrate that the unknowns generated by grand challenges represent a densification and complexity beyond those traditionally managed, necessitating new knowledge management processes. From a scientific perspective, this research enriches the understanding of unknowns in design activities and adapts this concept to the context of grand challenges. From a practical perspective, it provides actionable insights for engineering departments to address these unknowns independently, without relying on systemic coordination.</div
Federated Learning with Mixed Encryption for Distributed Energy Resources: Forecasting Electricity Charging Demand at Multiple Electrical Vehicle Charging Stations
No full textInternational audienceSharing information or data between actors iscrucial for forecasting distributed energy resources. However,collaboration involving the exchange of confidential data re-quires privacy-preserving models. Current models rely solelyon fully encrypted frameworks, neglecting publicly availabledata or enforcing encryption on both private and public data.Furthermore, it is unknown whether a combination of publicand private data types is beneficial to federated learning onspatiotemporal energy applications, in particular forecasting. Inthis work, we introduce a mixed encryption setting incorporatingpublic data (e.g. weather, calendar) and private data relative toenergy measurements. In this case study of day-ahead electricconsumption of neighboring Electric Vehicle charging stations,we show that combining data increases the accuracy by up to6% in RMSE and that mixed encryption reduces the computationtime by up to 2 times compared to full encryption