HAL-MINES ParisTech
Not a member yet
27348 research outputs found
Sort by
Comparison of the environmental impacts of perovskite/silicon tandem and silicon heterojunction photovoltaic modules using a parameterised approach
No full textInternational audienc
Assessment of common hypotheses adopted in borehole sizing for Ground-Source Heat Pump (GSHP) systems
No full textInternational audienc
Spline Interpolation on Compact Riemannian Manifolds
No full textSpline interpolation is a widely used class of methods for solving interpolation problems by constructing smooth interpolants that minimize a regularized energy functional involving the Laplacian operator. While many existing approaches focus on Euclidean domains or the sphere, relying on the spectral properties of the Laplacian, this work introduces a method for spline interpolation on general manifolds by exploiting its equivalence with kriging. Specifically, the proposed approach uses finite element approximations of random fields defined over the manifold, based on Gaussian Markov Random Fields and a discretization of the Laplace-Beltrami operator on a triangulated mesh. This framework enables the modeling of spatial fields with smooth variations and local anisotropies via domain deformation. The method is first validated on the sphere using both analytical test cases and a pollution-related study, and is compared to the classical spherical harmonics-based method. Additional experiments on the surface of a cylinder further illustrate the generality of the approach
All-sky Searches for Continuous Gravitational Waves from Isolated Neutron Stars in the Data from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run
No full textInternational audienceWe present results from an all-sky search for continuous gravitational waves, using three different methods applied to the first eight months of LIGO data from the fourth LIGO-Virgo-KAGRA Collaboration s observing run. We aim at signals potentially emitted by rotating, non-axisymmetric isolated neutron star in the Milky Way. The analysis spans a frequency range from 20 Hz to 2000 Hz and accommodates frequency derivative magnitudes up to Hz/s. No statistically significant periodic gravitational wave signals were detected. We establish 95% confidence-level (CL) frequentist upper limits on the dimensionless strain amplitudes. The most stringent population-averaged strain upper limits reach 9.7 near 290 Hz, matching the best previous constraints from 250 to 1700 Hz while extending coverage to a much broader spin-down range. At higher frequencies, the new limits improve upon previous results by factors of approximately 1.6. These constraints are applied to three astrophysical scenarios: 1) the distribution of galactic neutron stars as a function of spin frequency and ellipticity; 2) the contribution of millisecond pulsars to the GeV excess near the galactic center; and 3) the possible dark matter fraction composed of nearby inspiraling primordial binary black holes with asteroid-scale masses
Observer design for hybrid systems with partially affine forms and known jump times: Applications to walking robots
No full textInternational audienceWe propose novel observer designs for hybrid systems, with a certain nonlinear structure that is affine with respect to certain state components and with known jump times, based on decomposing the state into parts exhibiting different observability properties. We assume that the state of the hybrid system can be decomposed into two components. During flows, the first component is independent of the second one and is assumed to be instantaneously observable from the flow output. The second one is required to be either detectable or backward distinguishable via the combination of flows and jumps, from the jump output as well as a fictitious output describing how this second part impacts the first one at jumps. An observer is designed to estimate each component: a high-gain flow-based observer using the flow output estimates the first one and an LMI/KKL-based jump-based observer using an extended jump output estimates the second one. Global exponential convergence and stability of the estimation error in the original coordinates are proven using Lyapunov analysis. The proposed observers are exercised in the problem of estimating the state and uncertainties at impacts in a bipedal walking robot
La recherche partenariale : une alliance stratégique entre recherche fondamentale et monde industrie
No full textLa Fabrique de l'IndustrieDans une économie fondée sur la connaissance, les collaborations entre universités et entreprises jouent un rôle central dans l’innovation, la croissance économique et la transformation des sociétés. Loin de se limiter à un simple transfert de technologies ou à une sous-traitance de compétences scientifiques, ces partenariats sont aujourd’hui des espaces structurants de coconstruction des savoirs, où se redéfinit la frontière entre recherche académique et monde industriel
3D near-surface geophysics and geostatistics for heterogeneities characterization and water table monitoring on the Orgeval critical zone observatory
No full textInternational audienceGroundwater fluxes and interacting zones between groundwater and surface water are crucial for understanding the water dynamics of the critical zone. Groundwater within the critical zone plays a significant role in the ecosystem, biodiversity, and water supply. However, estimating these fluxes remains a key challenge because they are not directly measured in the field. Model calibration involves adjusting key parameters—such as saturated hydraulic conductivity and soil-water retention properties—using observed data like hydraulic head and river discharge, while initial and boundary conditions are prescribed to define the model l setup. That calibration is often done by comparing simulated soil water saturation and water table level to piezometers. Nevertheless, real flows occur in 3D in a complex medium containing heterogeneities with various lithologies, with different hydraulic parameters such as hydraulic conductivity and porosities.Geophysical methods such as electrical resistivity tomography (ERT), seismic refraction tomography (SRT), and multichannel analysis of surface wave (MASW), which are sensitive to lithology, content, and nature of fluid, represent helpful tools for hydrogeological modelling, both in terms of model parameterization and physical property characterization. ERT, which is particularly sensitive to lithology, allows us to identify and delineate heterogeneities, while seismic methods, which are sensitive to mechanical properties, will enable us to infer the water saturation and the piezometric surface in the near surface through the P-wave and S-wave velocities ratio (Poisson’s ratio, e.g. ) (Dangeard et al., 2021).We propose a workflow combining geophysics and geostatistics to reconstruct the heterogeneities and the piezometric surface in an alluvial plain context. We implemented the workflow in a 30 x 30 m area at the Avenelles site of the Orgeval Critical Zone Observatory (CZO), which is part of the French network of CZOs OZCAR. ERT, SRT, and MASW surveys were carried out along 7 profiles to obtain 2D sections of electrical resistivity, P and S wave velocities (6 profiles of 72 electrodes/geophones and one profile of 48 electrodes/geophones, with 0.40 m spacing leading to 12,708 apparent resistivity data, 33,888 first wave arrival picks, and 277 dispersion curves). Geophysics allows us to pass from punctual piezometer data to 2D vertical sections. However, carrying out 3D geophysical acquisition is cumbersome. To overcome these limitations, we then use geostatistics to get a distribution of our geophysical parameters in the 3D volume delineated by the geophysical survey. Once the 3D interpolation is done by kriging methods, we can retrieve a view of the heterogeneities distribution in the near surface as well as the water table position to inform hydrogeological inversion. Furthermore, with the addition of petrophysical relationships, it is possible to estimate saturation and porosity distribution for a future 3D hydrogeological physics-based model run to better characterise groundwater fluxes. Finally, all these workflows, including complementary methods, could be performed on different dates for time-lapse monitoring of the water table
Stochastic Coefficient of Variation: Assessing the Variability and Forecastability of Solar Irradiance
No full textInternational audienceThis work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation () and the Forecastability (). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. extends by integrating temporal dependencies via maximum autocorrelation, thus linking with . The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that and proficiently encapsulate multi-scale fluctuations, while addressing significant limitations inherent in traditional metrics. This comprehensive framework enables a refined quantification of solar forecast uncertainty, supporting improved decision-making in flexibility procurement and operational strategies. By assessing variability and forecastability across multiple time scales, it enhances real-time monitoring capabilities and informs adaptive energy management approaches, such as dynamic outage management and risk-adjusted capacity allocatio
Making soils into carbon sinks. A sociology of soil carbon quantification
No full textInternational audienceSince COP21 in 2015, carbon neutrality targets have emphasized the enhancement of various carbon sinks, including soils, to help sequester carbon away from the atmosphere. What does it take to make soils into carbon sinks? This article focuses on a digital model of soil carbon cycling (AMG), which quantifies soil carbon stocks and their evolution under various agricultural practices. We examine how AMG circulates and transforms within a loosely connected network of actors and organizations involved in agricultural development, climate research, land-use planning public administrations, and carbon commodification. We show how the model evolves into three distinct yet interconnected regimes of carbon quantification – in climate academic research, local public action, and carbon markets – that contribute to building and expanding a sociotechnical infrastructure for quantifying soil carbon sequestration potential. Our findings contribute to literature on environmental quantification and knowledge infrastructures by calling for a shift from viewing knowledge infrastructures as stable and fixed toward an approach that emphasizes open-ended, flexible, and ongoing processes of infrastructuring. We also contribute to the literature on soil/human relationships by emphasizing how the model fosters a new focus on the active role of soils in the global carbon cycle and climate change mitigation
Short-term forecasting of energy production and consumption using extreme learning machine: A comprehensive MIMO based ELM approach
No full textInternational audienceA Multiple-Input Multiple-Output (MIMO) Extreme Learning Machine (ELM) is introduced for short-term forecasting of seven grid variables in Corsica (France): total demand and generation from solar, wind, hydropower, thermal, bioenergy, and imports. Based on six years of hourly data, the model integrates sliding windows and cyclic time encodings to handle non-stationarity and seasonal effects without heavy preprocessing. At a 1-hour horizon, solar and thermal achieve nRMSE of 0.179 and 0.051 with R2 > 0.98, while total demand forecasts remain reliable up to five hours ahead. Wind and bioenergy remain challenging due to high intrinsic variability, but overall accuracy is robust across sources. Compared with persistence and an LSTM configured under realistic tuning budgets, MIMO -ELM consistently improves skill, offering small but stable gains over Single-Input Single-Output models (SISO). Beyond accuracy, the closed-form solution ensures fast training and suitability for real-time updates, enabling potential use in online learning contexts.A key advantage of the MIMO formulation is internal coherence between aggregate demand and its components, an important requirement for operators. The methodology adapts to local constraints such as grid characteristics, resource availability, and market structures, ensuring transferability beyond the Corsican case. The study shows that a parsimonious approach such as MIMO -ELM can deliver forecasts that are accurate, coherent, and computationally efficient, providing a practical decision-support tool for energy management and renewable integration.</p