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Dynamic Relationship Between Oil Temperature and BGCI in Bell 407 Helicopter
No full textInternational audienceThe primary objective of this study was to investigate the dynamic relationship between oil temperature and the Bearing Gearbox Condition Indicator (BGCI) values of the Bell 407 helicopter. The study aims to simplify the fault diagnosis process by proposing a method that utilizes only one vibration sensor and one temperature sensor per bearing. To achieve this goal, we employ robust econometric tools, such as unit root tests, cointegration tests, and Autoregressive Distributed Lag (ARDL) models, for both long-run and short-run estimates. Our findings indicate that the variable temperature tends to converge to its long-run equilibrium path in response to changes in other variables. The results of the ARDL analysis confirmed that spectral kurtosis, inner race, cage, and ball energy significantly contributed to the increase in temperature. Furthermore, we utilized the Impulse Response Function (IRF) to trace the dynamic response paths of the shocks to the system. The identification of a cointegrating relationship between oil temperature and BGCI values suggests a practical and significant connection that can potentially be used to predict hazardous changes in oil temperature using BGCI values, which is an important implication for enhancing the safety and reliability of helicopter operations.The study presents a promising direction for condition monitoring (CM) in rotating machinery, emphasizing the potential of integrating temperature data to simplify the diagnostic process while still achieving reliable results.</div
Analytical Modeling of Photovoltaic Systems Under Partial Shading Conditions Incorporating Bypass and Blocking Diodes Influence
No full textInternational audienceThis paper presents an innovative analytical model for photovoltaic (PV) systems operating under partial shading conditions (PSCs). The model is developed through a detailed analysis of the current–voltage ( I – V ) curves of PV systems affected by PSCs and employs a straightforward computational algorithm by adjusting every time the number of cells or modules contributing to power generation and calculating the voltage across these cells or modules, the Newton–Raphson algorithm is then used to solve the developed analytical model efficiently, ensuring accurate integration of all data into the equation of the output current. The proposed model accounts for the effects of bypass and blocking diodes, which are critical for managing partial shading and ensuring efficient power flow. It is applicable to various configurations, including individual PV modules and PV arrays in series (S), parallel (P), and series‐parallel (SP) arrangements. The results are validated through comparisons with Simpowersystem tools in the MATLAB‐Simulink environment. The model showcases notably accelerated execution times and dependable convergence toward the global maximum power point (GMPP). Additionally, the proposed algorithm can be implemented using any computational software, highlighting its versatility and potential for practical applications in PV system optimization and real‐time simulation environments
In Situ Characterization of Lithium-Ion Battery Dynamic Impedance Spectrum for Real-World Driving Applications
No full textInternational audienceDynamic electrochemical impedance spectroscopy (DEIS) provides critical insights into the kinetic pathways of Lithium-ion batteries under dynamic operating conditions, establishing its significance in advanced onboard diagnostics. However, accurate DEIS implementation faces challenges due to voltage drifts arising from load fluctuations, state of charge (SOC) variations, and temperature changes. To address these issues, this work proposes a novel differencing framework that systematically suppresses drifting components while preserving the integrity of perturbation and response signals, enabling precise in situ frequency response analysis. Analytical criteria are established to select differencing lags and orders, effectively mitigating disturbances without compromising frequency-domain responses under dynamic driving conditions. Comprehensive experimental investigations across diverse SOC levels, driving profiles, thermal environments, and battery aging states validate the method, achieving consistently accurate and stable DEIS measurements with maximum absolute residuals below 0.6% under Kramers-Kronig validatio
Layer-wise compaction 3D printing: void reduction and interfacial enhancement for continuous carbon fiber–reinforced thermoplastics
No full textInternational audienceThis study incorporated a heating roller into a 3D printer based on fused filament fabrication to develop a layer-wise compaction 3D printer. The heating roller facilitates heat compaction during 3D printing to reduce voids and enhance the interface. Coupon specimens were 3D-printed using a continuous carbon fiber-reinforced nylon composite, and the effects of the process on the microstructure and mechanical properties were analyzed. The porosity was measured using X-ray computed tomography, which demonstrated an effective reduction due to the heat-compaction process. The enhanced mode-I interlaminar fracture toughness was assessed using a double-cantilever beam test. The reduced voids and improved interface led to enhanced bending properties of the composite. The bending fracture surface exhibited a reduced compression failure area as a result of the heat compaction process, indicating an improvement in the compression load-bearing capacit
On the risk of fatigue failure of structural elements exposed to bottom wave slamming – Impulse response regime
No full textInternational audienceThis study aims to investigate whether fatigue damage induced by bottom wave slamming can be a failure mode, important to consider when sizing a marine structural element. The body exposed to wave impacts is assumed to have a shape and structural arrangement such that the duration of wave-impact loads is short relative to the structure's vibratory response time. In this dynamical regime, fatigue is found to be a potentially important failure mechanism: accounting for the risk of failure due to fatigue damage may result in design constraints that are significantly more conservative than those based on the risk of ultimate strength exceedance. The role of fatigue damage depends on the elevation of the body. It is predominant for low elevations, for which slamming events are frequent. Since this study aims to provide general insight, the specific details of the body, such as its shape and structural arrangement, are not specified. Instead, a general framework is used for the analysis. The way forward to address a specific case study, possibly including the effects of forward and seakeeping motions, is briefly explained
Estimation de l'état caché et de paramètres stochastiques avec filtres particulaire et de Kalman d'ensemble combinés
No full textInternational audienceQuantifying uncertainties is a key aspect of data assimilation systems since it has a large impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms, such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additive Gaussian noises and use both inflation and localization to avoid filter degeneracy and compensate for misspecifications. This introduces different stochastic parameters which need to be carefully estimated in order to get a reliable estimate of the latent state of the system. A classical approach to estimate unknown parameters in data assimilation consists in using state-augmentation, where the unknown parameters are included in the latent space and are updated at each iteration of the EnKF. However, it is well-known that this approach is not efficient to estimate stochastic parameters because of the complex (non-Gaussian and non-linear) relationship between the observations and the stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm fails to estimate high-dimensional systems due to the curse of dimensionality. The strengths of these two methods are gathered in the proposed algorithm, where the PF first generates the particles that estimate the stochastic parameters, then using the mean particle the EnKF generates the members that estimate the geophysical variables. This generic method is first detailed for the estimation of parameters related to the model or observation error and then for the joint estimation of inflation and localization parameters. Numerical experiments are performed using the Lorenz-96 model to compare our approach with state-of-the-art methods. The results show the ability of the new method to retrieve the geophysical state and to estimate online time-dependent stochastic parameters. The algorithm can be easily built from an existing EnKF with low additional cost and without further running the dynamical model.La quantification des incertitudes est fondamentale en assimilation de données du fait du large impact sur la qualité des prédictions et des analyses. Les algorithmes d'assimilation de données séquentielle, comme le filtre de Kalman d'ensemble (EnKF), représentent les erreurs de modèle et d'observation par des bruits Gaussiens additifs et utilisent l'inflation et la localisation pour éviter la dégénérescence du filtre et compenser les mauvaises spécifications. Cela amène à scrupuleusement estimer différents paramètres stochastiques pour avoir une bonne estimation de l'état caché du système. Une approche classique pour estimer des paramètres en assimilation de données consiste à utiliser l'augmentation de l'état caché, où les paramètres inconnus sont inclus dans l'espace latent et estimés à chaque itération de l'EnKF. Cependant, il est bien connu que cette approche est inefficace pour estimer des paramètres stochastiques du fait de la complexe relation (non Gaussienne et non linéaire) entre les observations et les paramètres stochastiques qui ne peut être gérée par l'EnKF. Une alternative pour estimer des modèles espace-état non Gaussiens et non linéaires est d'utiliser un filtre particulaire (PF), mais cet algorithme peine à estimer des systèmes de grande dimension à cause de la malédiction de la dimension. Les forces de ces deux méthodes sont réunies dans l'algorithme proposé, où le PF génère en premier les particules estimant les paramètres stochastiques, en utilisant ensuite la particule moyenne l'EnKF génère les membres estimant les variables géophysiques. Cette méthode générique est dans un premier temps détaillée pour estimer les paramètres associés aux erreurs de modèle ou d'observation, puis pour estimer conjointement les paramètres de d'inflation et de localisation. Des résultats numériques sont réalisés en utilisant le modèle de Lorenz-96 pour comparer notre approche avec des méthodes de la littérature. Les résultats montrent l'efficacité de la nouvelle méthode à retrouver l'état géophysique et à estimer en ligne des paramètres stochastiques variant en temps. L'algorithme peut aisément être implémenté à partir d'un EnKF existant, avec un faible coût algorithmique additionnel et sans davantage utiliser le modèle dynamique
Multifactored accelerated marine corrosion of immersed steels influenced by washed ashore Sargassum rafts
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A Pan-European study of the bacterial plastisphere diversity along river-to-sea continuums
No full textInternational audienceMicroplastics provide a persistent substrate that can facilitate the transport of microbes from one ecosystem to another. Since most marine plastic debris originates from land and is carried to the ocean by rivers, a significant concern about the plastisphere is the potential dispersal of freshwater bacteria into the sea. To address this question, we explored the plastisphere on microplastic debris (MPs) and on pristine microplastics (pMPs) as well as the bacteria living in surrounding waters, along the river-sea continuum in nine major European rivers sampled during the seven months of the Tara Microplastics mission. In both marine and riverine waters, we found a clear niche partitioning among MPs and pMPs plastispheres when compared to the bacteria living in the surrounding waters. Among the large dataset, we found a clear gradient of bacterial community structure from the freshwater to the sea, with a complete segregation in plastisphere composition between the two ecosystems. We also described for the first time a virulent human pathogenic bacteria on MPs (Shewanella putrefaciens) able to infect human intestinal epithelial cells, that was only detected in river. Our results reinforce the major role played by the environmental conditions in shaping plastisphere biodiversity, that is not consistent with a critical transfer of pathogens between freshwater and seawater ecosystems
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
No full textInternational audienceUnsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets. Traditionally, this involves using dimensionality reduction (DR) methods to project data onto lower-dimensional spaces or organizing points into meaningful clusters (clustering). In this work, we revisit these approaches under the lens of optimal transport and exhibit relationships with the Gromov-Wasserstein problem. This unveils a new general framework, called distributional reduction, that recovers DR and clustering as special cases and allows addressing them jointly within a single optimization problem. We empirically demonstrate its relevance to the identification of low-dimensional prototypes representing data at different scales, across multiple image and genomic datasets
Aluminum Oxide Films and Sapphire Demonstrate Interaction of Sample Preparation and Instrumentation for XPS Measurements of Layered Materials
No full textInternational audienceLayered materials have multiple applications in electronics and energy storage, including gate dielectrics, diffusion barriers, surface coatings, and other devices. X-ray photoelectron spectroscopy (XPS) is often used to characterize the chemistry of layered materials. However, before meaningful analysis of materials can be made by XPS, there is a need to investigate how a particular sample interacts with XPS instrumentation. Thin aluminum oxide films are promising optoelectronic materials due to their optical, chemical, and electrical properties. In this work, an example of aluminum foil is used to demonstrate the consequences of choices made when XPS is performed on a material with an oxide overlayer on a bulk metal substrate. The results presented demonstrate how these choices alter the spectra measured, and through extended periods of acquisition cycles, the response of a sample to XPS can be characterized, understood, and mitigated by experimental design and appropriate peak model construction