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Experimental Characterization of Screw-Extruded Carbon Fibre-Reinforced Polyamide: Design for Aeronautical Mould Preforms with Multiphysics Computational Guidance
Get PDFPublisher Copyright: © 2024 by the authors.In this research work, the suitability of short carbon fibre-reinforced polyamide 6 in pellet form for printing an aeronautical mould preform with specific thermomechanical requirements is investigated. This research study is based on an extensive experimental characterization campaign, in which the principal mechanical properties of the printed material are determined. Furthermore, the temperature dependency of the material properties is characterized by testing samples at different temperatures for bead printing and stacking directions. Additionally, the thermal properties of the material are characterized, including the coefficient of thermal expansion. Moreover, the influence of printing machine parameters is evaluated by comparing the obtained tensile moduli and strengths of several manufactured samples at room temperature. The results show that the moduli and strengths can vary from 78% to 112% and from 55% to 87%, respectively. Based on a real case study of its aeronautical use and on the experimental data from the characterization stage, a new mould design is iteratively developed with multiphysics computational guidance, considering 3D printing features and limitations. Specific design drivers are identified from the observed material’s thermomechanical performance. The designed mould, whose mass is reduced around 90% in comparison to that of the original invar design, is numerically proven to fulfil thermal and mechanical requirements with a high performance.Peer reviewe
Condition monitoring of mooring systems for Floating Offshore Wind Turbines using Convolutional Neural Network framework coupled with Autoregressive coefficients
No full textPublisher Copyright: © 2024 Elsevier LtdThis research presents a novel approach proposed for the monitoring of mooring systems in Floating Offshore Wind Turbines (FOWTs), employing a combination of Convolutional Neural Networks (CNNs) and Auto-Regressive (AR) models. CNN finds broad application in monitoring intricate structures, as they adeptly handle noisy response data without necessitating profound domain expertise. The precision of CNNs relies on the extraction of meaningful features from input data, necessitating meticulous data curation and labeling for optimal computational efficiency and accurate estimation. Emphasis is placed on the preference for feature-rich small datasets over voluminous yet sparse datasets, aiming to enable CNNs to discern crucial patterns more effectively and mitigate issues such as overfitting and extensive preprocessing. The novelty of the proposed approach lies in the integration of AR models, which serve to compress data and enhance damage-sensitive characteristics in the input for CNNs. This integration involves deploying regression models fitted to historical responses, parameterized with AR coefficients sensitive to damage, and further classifying severity using CNNs. The sequential nature of this approach addresses challenges such as vanishing/exploding gradients, particularly for extended historical data, while also attenuating the impact of noise and irrelevant information through data compression. The study explores the effectiveness of the coupled AR-CNN method in monitoring FOWT mooring lines, with a specific focus on two levels of damage identification: detection with classification and damage severity across diverse damage and operational scenarios. The modified methodology exhibits superior outcomes by conducting a performance analysis against traditional CNNs and other machine-learning methods, highlighting the potential of the AR-CNN strategy to improve the precision of FOWT mooring line condition monitoring. These findings underscore the AR-CNN strategy's potential to enhance the accuracy of FOWT mooring line condition monitoring.Peer reviewe
Boosting Data Monetisation with DATAMITE
No full textPublisher Copyright: © IFIP International Federation for Information Processing 2024.Companies around the globe store large quantities of data they cannot monetise. Regarding internal monetisation, they lack tools to facilitate the governance and quality assessment of their data, resulting not really knowing what data they own or it being non reliable due to its poor quality. External monetisation is usually hindered by the unavailability of trustable mechanisms to perform this exchange or enabling the company to participate in ecosystems like EU data spaces or Gaia-X. DATAMITE is an open-source modular and multi-domain framework that focuses on monetisation through interoperability and data exchange. Its modules offer tools for enhancing data governance, quality and security, but also enabling data sharing to a collection of ecosystems like data spaces, Gaia-X, EOSC or AIoD through a plugin-based approach. It also includes a series of additional support tools to assist on data discovery, ingestion, harmonization or evaluate data fairness, among other.Peer reviewe
Influence of the Manufacturing Method on the Fire Resistance of Geopolymer Materials Based on Mining Slag
Get PDFPublisher Copyright: © 2024 Seventh Sense Research Group®.Developing geopolymer materials based on waste is being promoted as an approach to reduce landfilling and encourage a circular economy. In this regard, high-performance geopolymers based on mining slag are developed for fire protection products, where the manufacturing method could have an influence. Accordingly, this paper assesses the fire resistance performance of two geopolymer products based on the same slag but produced considering two different manufacturing processes (precast and 3D printed), mainly focused on their use for tunnels. Furthermore, it studies other fire resistance evaluation methods (laboratory tests at different scales, in-situ tests, and computer based simulations), identifying their suitability for product development or research phases. On the one hand, results show that the production method affects the fire resistance performance since tested geopolymers reveal different thermal transmittance and mechanical behavior in prolonged or extreme fire exposure due to the diverse nature of the geopolymer material itself the first one is ductile material while the second a brittle material. In this sense, the 3D printed material shows a better thermal performance, but this can be significantly affected by the fastening configuration used. On the other hand, a step-by-step methodology based on the combination of the different fire resistance evaluation methods is presented to facilitate the product assessment during the various product development stages and for different system configurations or end-use applications.Peer reviewe
Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework
No full textPublisher Copyright: © The Author(s) 2024.We propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.Peer reviewe
Ingestible pill for the detection of inflammatory bowel diseases
Get PDFPublisher Copyright: © 2024, Avestia Publishing. All rights reserved.Gastrointestinal inflammatory and immune-based diseases, such as ulcerative colitis and Crohn's disease, are becoming more common and appearing at younger ages. These diseases are believed to be caused by an abnormal activation of the immune system in a genetically susceptible host against elements of the enteric microbiota, which triggers the inflammatory mechanisms that lead to intestinal injury [1]. Although their etiology is not fully understood [2], it is accepted that a complex interaction between the host's immune system, genetics, microbiota, and environmental factors is the most likely causal agent, in which the imbalance between proinflammatory and anti-inflammatory cytokines and the alteration of the composition and function of the intestinal microbiota (dysbiosis) play a fundamental role[3]. In summary, inflammatory bowel diseases (IBD) are caused by an abnormal activation of the immune system in a genetically susceptible host against elements of the enteric microbiota, which triggers the inflammatory mechanisms that lead to intestinal injury. Therefore, effective in-situ monitoring of relevant biomarkers in the gastrointestinal tract (GI) plays a decisive role in the early diagnosis and treatment of these disorders [4]. Currently, the diagnosis of IBD is performed either through stool analysis or endoscopic techniques, which, in addition to being invasive technologies that involve tissue biopsy, are complex to access certain regions of the digestive tract and do not allow for patient monitoring over prolonged periods of time. The ONBODY project aims to develop a miniaturized ingestible device that allows for early, reliable, and real-time diagnosis of these diseases through the detection of cytokines that regulate the immune and inflammatory response in the GI. The present work reports the development of a biocompatible capsule with immunoelectrochemical-based sensors for detecting proinflammatory and anti-inflammatory related cytokines. The electronic sensing module is based on a high precision, electrochemical front end which allows potentiometric, amperometric and impedimetric measurements. A low-power MCU is used to control the system and the measuring module in a time predefined basis or under request, and a NFC chip allows waking up the system from an external device and sending back the measured information, using a coil antenna of 1cm diameter wrapped around the electronics. Each of these sensing, processing and communication modules are integrated into a 9.6mm diameter round rigiflex printed circuit boards (PCB) with the flex interconnection between PCBs allowing the stacking of the three boards with robust and reliable connections in a miniaturized form factor. All the electronic components are power supplied by two silver-oxide batteries of 9.5mm diameter and 5.4 mm height with 82 mA/h capacity. A combination of engineered materials is designed to shelter the electronic components in a non-degradable capsule, make the sensing compartment permeable to liquid samples, and prevent the components' degradation and exposure along the GI tract's acid section, which ensures a specific actuation of the pill from the intestine.Peer reviewe
Dynamic thermal management control solution for an air-cooled automotive Lithium Ion battery pack
No full textPublisher Copyright: © 2024 IEEE.The performance and life-cycle of an automotive Lithium Ion (Li-Ion) battery pack is heavily influenced by its operating temperatures. For that reason, a Battery Thermal Management System (BTMS) must be used to constrain the core temperatures of the cells between 20°C and 40°C. In this work, an accurate electro-thermal model is developed for cell temperature estimation. A cascaded thermal management control loop is then proposed to dynamically keep battery temperatures within the predefined limit. Simulation results are provided under standardized urban driving cycle conditions, demonstrating the correct operation of the proposal.Peer reviewe
Modulating the Fidelity and Spatial Extent of Electrotactile Stimulation to Elicit the Embodiment of a Virtual Hand
No full textPublisher Copyright: IEEERestoring tactile feedback in virtual reality can improve user experience and facilitate embodiment. Electrotactile stimulation is an attractive technology in this context because it is compact and allows for high-resolution spatially distributed stimulation. In this study, a 32-channel tactile glove worn on the fingertips was used to provide tactile sensations during a virtual version of a rubber hand illusion experiment. To assess the benefits of multichannel stimulation, we modulated the spatial extent and fidelity of feedback. Thirty-six participants performed the experiment in two conditions, where stimulation was delivered to a single finger or all fingers, and three tactile stimulation types within each condition: no tactile feedback, simple single-point stimulation, and complex sliding stimulation mimicking the brush movements. Following each trial, the participants answered a multi-item embodiment questionnaire and reported the proprioceptive drift. The results confirmed that modulating the spatial extent of stimulation, from a single finger to all fingers, was indeed a successful strategy. When stimulating all fingers, tactile feedback significantly improved all subjective measures compared to receiving no tactile stimulation. However, unexpectedly, the second strategy, that of modulating the fidelity of feedback, was not successful since there was no difference between the simple and complex tactile feedback in any of the measures. The results, therefore, imply that the effects of tactile feedback are better expressed in a more dynamic scenario (i.e., making/breaking contact and stimulating different body locations), while it should be investigated if further improvements of the complex feedback can make it more effective than the simple approach.Peer reviewe
Exploring Utility in a Real-World Warehouse Optimization Problem: Formulation Based on Quantum Annealers and Preliminary Results
No full textPublisher Copyright: © 2024 IEEE.In the current NISQ-era, one of the major challenges faced by researchers and practitioners lies in figuring out how to combine quantum and classical computing in the most efficient and innovative way. In this paper, we present a mechanism coined as Quantum Initialization for Warehouse Optimization Problem that resorts to D-Wave's Quantum Annealer. The module has been specifically designed to be embedded into already-existing classical software dedicated to the optimization of a real-world industrial problem. We preliminary tested the implemented mechanism through a two-phase experimentation against the classical version of the software.Peer reviewe
Ensemble deep learning for Alzheimer’s disease characterization and estimation
No full textPublisher Copyright: © Springer Nature America, Inc. 2024.Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands.Peer reviewe