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Design methodology for the identification and Scaling of nature-based solution for carbon dioxyde Capture and storage
No full textInternational audienceThis article addresses the urgent need to combat climate change by exploring Nature-based Solutions(NbS) for carbon capture, utilization, and storage (CCUS). The existing CCUS technologies facechallenges of maturity, energy intensity, and high costs. To answer this challenge, this work proposesa new design methodology to facilitate the identification, evaluation and scaling of NbS for CCUS in asystematic and impactful manner.The proposed method involves analyzing a problem, identifying the required function, and usingkeywords to guide the research. Through iteration, the array of potential solutions expands, providingdiverse options with various biological functions. Then, the focus shifts to narrowing down the mostpromising solutions to reduce the number of potential NbS. Common functions are identified, solutionsare organized by domain, and priorities are set based on ability, effectiveness, and cost. Finally,scalability insights, as well as the enablers and barriers, are identified to design a roadmap for scalingthe NbS.Applied to the case study of CCUS, 68 NbS emerged, including bivalve, and carbonic anhydrase forexample. The method then narrowed down the research to five top solutions: cactus, mycorrhizal fungi,microalgae, cobalt oxide and rocks (basalt, olivine) for enhanced weathering. This approach facilitatesthe efficient identification of promising NbS. Subsequently, scalability insights and a roadmap weredeveloped, using the example of the Opuntia ficus-indica cactus, which was one of the five NbSselected
Sensorless control for a non-sinusoidal seven-phase Surface-mounted PM machine
No full textInternational audienceThe paper addresses the position sensorless control of a seven-phase Surface-mounted permanent magnet (SPM) machine with highly non-sinusoidal back-emf, which is termed bi-harmonic as fundamental and third harmonic are of the same order. The control is implemented through a vector decomposition of the seven-phase machine into three magnetically independent virtual machines, each of them been controlled in its own rotating frame. Three electromotive-force methods to evaluate the rotor position can thus be achieved and the method will be all the robust as the current of the relative virtual machine is null. Since the rated control aims at producing the torque from the first and the third harmonics, the fifth harmonic of the back-emf is used to estimated the position. Simulation and experimental tests show the real possibility of this approach. In particular, an original and specific startup procedure is developed, that is based on the sequential use of different virtual machines.</div
Oak timber cross-cutting based on fiber orientation scanning and mechanical modelling to ensure finger-joints strength
No full textInternational audienceThe mechanical properties of wood depend on its characteristics at different scales, in particular its knots and the orientation of its fibers. As wood is a highly anisotropic material, its strength and elastic properties are much better in the longitudinal direction of the fibers than in the perpendicular directions, so the orientation of the fibers is an extremely important parameter. Finger-jointing is a technique for joining sawn timbers into large panels for structural use, such as finger-jointed panels (GLT) and laminated timbers (CLT). In this study, the tensile strength of oak sawn timbers was modelled by considering the local fiber orientation obtained by using laser diffusion patterns onto the timber faces. Strength thresholds were established to ensure an equivalent mechanical performance to T11 tensile strength class, with the possibility of cross-sectional splitting if required. In addition, the tensile strength of the finger-joints was determined in accordance with (NF EN 14080 2013). Lamellas were assembled, glued and subjected to tensile tests, and the method was found to be satisfactory in terms of characteristic strength of the finger joints
Knowledge Graph as Digital Twins Enhancer for Real Case Data-Driven Smart Building
No full textInternational audienceThe integration of data capture, analysis, monitoring, and control technologies is rapidly becoming the cornerstone of next-generation smart buildings. However, developing digital twins that dynamically interact with these buildings presents a significant challenge. In this paper, we study the most appropriate data models for leveraging a digital twin from data-driven smart buildings. We propose a framework that exploits a knowledge graph to directly address the challenges encountered in real-world building management systems, ensuring that the information is comprehensible as a preliminary step to intelligent decision-making. Furthermore, we validate this proposal for improving building performance and sustainability through a real-world use case. The experimental results, utilizing dynamic data streams from the Internet of Things (IoT), demonstrate promising outcomes. This research paves the way for using graph-based models and algorithms as digital twin enhancers for managing data-driven smart buildings
Optimizing Dynamic Optimization of a Reconfigurable Manufacturing System under Risk and Human Factors
No full textInternational audienceThe adaptability of Reconfigurable Manufacturing Systems (RMSs) to ever-changing product demand and their responsiveness to unpredictable events has made them a popular solution for modern manufacturing in today's competitive industries. The proper scheduling of RMSs is important in ensuring optimal resource utilization, achieving production objectives, and responding to dynamic changes in a realtime. This study aims to tackle job scheduling and workforce planning in RMSs by considering dynamic events, such as the risk of machine unavailability, and human factors in the manufacturing process, to minimize the makespan and delay-related cost. At first, a comprehensive framework for dynamic optimization in an RMS environment has been developed. A multi-level bi-objective mathematical modeling presented to address job scheduling and workforce planning proposed as a MILP model. Finally, numerical studies evaluated two scenarios: risk and human factors addressed or not, for small to mediumsized cases. The results demonstrate a significant gap between these scenarios
The Measurement of Spatiotemporal Parameters in Running at Different Velocities: A Comparison Between a GPS Unit and an Infrared Mat
No full textInternational audienceThe accurate measurement of spatiotemporal parameters, such as step length and step frequency, is crucial for analyzing running and sprinting performance. Traditional methods like video analysis and force platforms are either time consuming or limited in scope, prompting the need for more efficient technologies. This study evaluates the effectiveness of a commercial Global Positioning System (GPS) unit integrated with an Inertial Measurement Unit (IMU) in capturing these parameters during sprints at varying velocities. Five experienced male runners performed six 40 m sprints at three velocity conditions (S: Slow, M: Medium, F: Fast) while equipped with a GPS-IMU system and an optical system as the gold standard reference. A total of 398 steps were analyzed for this study. Step frequency, step length and step velocity were extracted and compared using statistical methods, including the coefficient of determination (r2) and root mean square error (RMSE). Results indicated a very large agreement between the embedded system and the reference system, for the step frequency (r2 = 0.92, RMSE = 0.14 Hz), for the step length (r2 = 0.91, RMSE = 0.07 m) and the step velocity (r2 = 0.99, RMSE = 0.17 m/s). The GPS-IMU system accurately measured spatiotemporal parameters across different running velocities, demonstrating low relative errors and high precision. This study demonstrates that GPS-IMU systems can provide comprehensive spatiotemporal data, making them valuable for both training and competition. The integration of these technologies offers practical benefits, helping coaches better understand and enhance running performance. Future improvements in sample rate acquisition GPS-IMU technology could further increase measurement accuracy and expand its application in elite sports
Caractérisation multiphysique de la peau et de substituts de peau pour la mise en place d’outils de diagnostic chez le grand brûlé
No full textInternational audienceLa peau est l’organe le plus grand du corps humain et joue un rôle crucial dans le maintien de son homéostasie. Elle protège ce dernier des agressions extérieures telle que les infections ou les changements dans l’environnement. Cette barrière vient à être altérée en cas de brûlure grave et la profondeur est parfois difficile à estimer de manière directe. Ainsi, une caractérisation multiphysique de la peau est nécessaire, aussi bien en milieu clinique, que sur des substituts permettant de reproduire ses propriétés mécaniques. Pour cela, des substituts à base de gel d’agar peuvent être utilisés ainsi que des polymères notamment à base de silicone. L’objectif est de caractériser ces substituts dans le but de créer des modèles personnalisables et adaptables selon les données que l’on obtient directement sur patient dans le cadre de campagnes d’essais cliniques. Pour cela, une méthode de compression sphérique avec deux points de contact est utilisée, couplée à de la modélisation par éléments finis
Estimation of Mill-Scale Thickness by Back-Propagation Neural Network for Pickling
No full textInternational audienceIn the production of hot-rolled steel strip, the formation of mill scale, a surface oxide layer, results. This byproduct, if not properly removed for subsequent processing, can lead to coating failure resulting in rapid corrosion. Removal, often by pickling in an acid bath, generates considerable toxic waste, and over pickling can also damage the steel surface. Knowledge of the mill-scale thickness is crucial for optimizing pickling, and consequently to ensure the longevity and integrity of steel products while minimizing toxic-waste production. To obtain a nondestructive measurement of mill-scale thickness, we use the technique of terahertz time-of-flight tomography. Due in some cases to the optically thin nature of the mill scale in the terahertz frequency range, we utilize a back-propagation neural network applied to the raw experimental data to rapidly and accurately estimate the mill-scale thickness. In this work, two neural-network approaches are implemented: one for regression and one for classification. Both networks take in the terahertz time-of-flight tomography data and output an estimation on the thickness of the mill scale, which ranges from ∼ 5 to 15 µm. The regression network has the ability to estimate the thickness of mill scale with RMSE error around 1.6 µm. The classification network is able to classify the samples into three categories according to their thickness range with an accuracy of 87% on the test measurements.<
Vers une maîtrise renforcée de la qualité dimensionnelle et des caractéristiques thermomécaniques des pièces forgées : développement d'un jumeau numérique pour asservir le pilotage en énergie d'une opération de mise en forme des matériaux
No full textThis doctoral research was carried out in the context of the digitization of forging processes, highlighting the difficulties encountered in small-scale production, where process flexibility is crucial. The central challenge was to ensure the reproducibility of forged parts despite the variable conditions inherent in manufacturing environments.The main objective of this research was to develop a digital twin (DT) capable of simulating, piloting and controlling forging operations. A crucial step in the creation of this DT was to develop robust prediction models. These models had to meet several challenges in order to be effectively integrated into a DT: they had to be reactive to provide real-time information to operators to facilitate decision-making during manufacturing, predictive to allow access to variables that cannot be directly measured, such as the thermomechanical paths of parts, and faithful to offer predictions as accurate as possible in relation to reality.To meet these requirements, this thesis proposed a structured methodology for the development of prediction models. The models chosen were surrogate models based on numerical simulations.Key steps in the methodology included setting up a predictive numerical simulation of the forging process under nominal conditions, using sensitivity analysis techniques to identify critical parameters influencing the final results, applying model reduction techniques to incorporate multi-scale variables such as thermomechanical fields into the prediction models, and finally, training and validating the surrogate models to ensure their accuracy and reliability, while respecting a computation time constraint of less than one second.Two specific case studies were examined in detail: uni-axial compression and an open-die M shape forging, each aimed at testing and fine-tuning the models developed.In conclusion, this research has opened up new perspectives in the application of digital twins to optimize the quality, reproducibility and efficiency of forging operations in the industrial sector.Cette thèse s'est inscrite dans le contexte de la numérisation des procédés de forgeage, en mettant en lumière les défis rencontrés dans les productions à petite échelle, où la flexibilité des procédés est cruciale. L'enjeu central résidait dans la nécessité d'assurer la reproductibilité des pièces forgées malgré les conditions variables inhérentes aux environnements de fabrication.L'objectif principal de cette recherche était de développer un jumeau numérique (JN) capable de simuler, piloter et contrôler les opérations de forgeage. Une étape cruciale dans la création de ce JN consistait à mettre au point des modèles de prédiction robustes. Ces modèles devaient relever plusieurs défis pour être efficacement intégrés dans un JN : ils devaient être réactifs pour fournir des informations en temps réel aux opérateurs afin de faciliter la prise de décision pendant la fabrication, prédictifs pour permettre l'accès à des variables non directement mesurables comme les chemins thermomécaniques des pièces, et fidèles pour offrir des prédictions aussi précises que possible par rapport à la réalité.Pour répondre à ces exigences, cette thèse a proposé une méthodologie structurée pour le développement de modèles de prédiction. Les modèles choisis étaient des modèles de substitution basés sur des simulations numériques.Les étapes clés de la méthodologie comprenaient la mise en place d'une simulation numérique prédictive du procédé de forgeage dans des conditions nominales, l'utilisation de techniques d'analyse de sensibilité pour identifier les paramètres critiques influençant les résultats finaux, l'application de techniques de réduction de modèle pour intégrer des variables à plusieurs échelles comme les champs thermomécaniques dans les modèles de prédiction, et enfin, l'entraînement et la validation des modèles de substitution pour assurer leur précision et leur fiabilité, tout en respectant une contrainte de temps de calcul inférieure à une seconde.Deux cas d'étude spécifiques ont été examinés en détail : la compression uni-axiale et une opération de forgeage en M, chacun visant à tester et à affiner les modèles développés.En conclusion, cette thèse a ouvert de nouvelles perspectives dans l'application des jumeaux numériques pour optimiser la qualité, la reproductibilité et l'efficacité des opérations de forgeage dans le secteur industriel
Enhancing Technology-Focused Entrepreneurship in Higher Education Institutions Ecosystem: Implementing Innovation Models in International Projects
No full textInternational audienceInnovation models are key to fostering technology-focused entrepreneurship in higher education institutions (HEIs). These models create dynamic environments that encourage collaboration, creativity, and problem-solving skills among students and faculty. HEIs face several challenges in fostering entrepreneurship, including allocating sufficient financial and human resources, integrating entrepreneurship education across disciplines, and managing intellectual property. Overcoming these challenges requires HEIs to cultivate an entrepreneurial culture and establish strong partnerships with industry stakeholders. To achieve these goals, HEIs must adopt successful innovation models proven to work. This article presents an international case study highlighting such models and the factors contributing to their success. This study explores the implementation and impact of innovation models, specifically IDEATION and DEETECHTIVE, within HEIs to foster technology-focused entrepreneurship. By implementing numerous actions focusing on online education integration and the Quintuple Helix Innovation Model, these models support shifting engineering students’ mindsets toward entrepreneurship. This research highlights the importance of academia–industry collaboration, international partnerships, and the integration of entrepreneurship education in technology-focused disciplines. This study presents two models. The first, IDEATION, focuses on open innovation and sharing economy aspects. This model underwent rigorous testing and refinement, evolving into the second model, DEETECHTIVE, which is more comprehensive and deep tech-focused. These models have been validated as effective frameworks for fostering entrepreneurship and innovation within HEIs. This study’s findings underscore the potential of these models to enhance innovation capacity, foster an entrepreneurial culture, and create ecosystems rich in creativity and advancement. Practical implications include the establishment of open innovation-oriented structures and mechanisms, the development of specialized curriculum components, and the creation of enhanced collaboration platforms