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Floating offshore wind turbine nonlinear model predictive control optimisation method
Get PDFPublisher Copyright: © 2024 The AuthorsThis paper presents a novel control parameter optimisation methodology for nonlinear model predictive control for floating offshore wind turbine operation, computing optimisation weights as environment conditions dependent variables. The main objective is to reduce the required time to define the optimal control parameters for the nonlinear control strategy, using an automated approach. To achieve this, an optimisation methodology based on extreme operational gust conditions is applied by employing a Random Walk-type Monte Carlo procedure. The primary aim is to introduce an advanced control design approach that addresses concerns related to the efficient power generation and longevity of floating systems, particularly considering the growing scale of wind turbines and the dynamic behaviour of floating platforms, which increase the system overall costs. The resulting optimised controller is also evaluated against state-of-the-art feedback-based control strategies in different operational environmental conditions.Peer reviewe
Functional printing for main distortion points in cured composite parts
No full textPublisher Copyright: © 2024 IEEE.Advancements in printing technologies have facilitated the integration of sensors within composite parts for aeronautical applications. Sensor characteristics were rigorously analyzed to meet the specific requirements of phenomena such as springback, affecting composite parts after curing. In this study, a Design of Experiments (DoE) was conducted to generate substantial data for training a surrogate model, which predicts distortions in L-shaped structures, thereby eliminating dependency on Finite Element Method (FEM) software. Utilizing the acquired insights, the paper further investigates the experimental study on sensor requirements encompassing location, operational temperature range, sensing capabilities, and signal acquisition during curing processes. The equipment and experimental setup are detailed to outline additional requirements for future research. This paper presents a comprehensive methodological approach for the development and characterization of sensors optimized for the curing of composite parts.Peer reviewe
Task Automation in Construction Sites: Robot Learning from Teleoperated Demonstrations
No full textPublisher Copyright: © 2024 IEEE.Easy, reliable and fast learning from demonstration approaches are desirable for task automation in construction sites. Unlike in classic assembly industry where the robot acts in a well-controlled setting, construction sites are unstructured and constantly changing environments. Therefore both workers and robots have to easily adapt to new working scenarios. In this paper we propose a method for automating the joint filling with mastic using a single human demonstration. The demonstration is performed by a human via teleoperation. The learning phase aims at estimating the appropriate tuning parameters of the admittance controller used to reproduce the human motion. Early stage laboratory testings presented in this document demonstrate the validity of the proposed learning scheme.Peer reviewe
Enhancing the Supercapacitive Behaviour of Cobalt Layered Hydroxides by 3D Structuring and Halide Substitution
Get PDFPublisher Copyright: © 2024 The Authors. Batteries & Supercaps published by Wiley-VCH GmbH.Among the two-dimensional (2D) materials, layered hydroxides (LHs) stand out due to their chemical versatility, allowing the modulation of physicochemical properties on demand. Specifically, LHs based on earth-abundant elements represent promising phases as electrode materials for energy storage and conversion. However, these materials exhibit significant drawbacks, such as low conductivity and in-plane packing that limits electrolyte diffusion. In this work, we explore the synthetic flexibility of α-CoII hydroxides (Simonkolleite-like structures) to overcome these limitations. We elucidate the growth mechanism of 3D flower-like α-CoII hydroxyhalides by using in situ SAXS experiments combined with thorough physicochemical, structural, and electrochemical characterization. Furthermore, we compared these findings with the most commonly employed Co-based LHs: β-Co(OH)₂ and CoAl layered double hydroxides. While α-CoII LH phases inherently grow as 2D materials, the use of ethanol (EtOH) triggers the formation of 3D arrangements of these layers, which surpass their 2D analogues in capacitive behavior. Additionally, by taking advantage of their anion-dependent bandgap, we demonstrate that substituting halides from chloride to iodide enhances capacitive behavior by more than 40 %. This finding confirms the role of halides in modulating the electronic properties of layered hydroxides, as supported by DFT+U calculations. Hence, this work provides fundamental insights into the 3D growth of α-CoII LH and the critical influence of morphology and halide substitution on their electrochemical performance for energy storage applications.Peer reviewe
Assessing Fixed and Moving Mesh Methods for Hydrodynamic Free Surface Simulation in Induction Melting
No full textPublisher Copyright: © 2024 75th World Foundry Congress, WFC 2024. All rights reserved.This study develops a coupled finite element method (FEM) approach to optimize induction-melting processes by accurately simulating multiphysics phenomena. It integrates a magneto-hydrodynamic model to represent magnetic fields and Lorentz forces, crucial for fluid flow and pressure fields. Comparing fixed and moving mesh techniques, it assesses their effectiveness in capturing free surface deformation. Experimental validation in an aluminum induction-melting furnace confirms the model's accuracy. Insights gained contribute to advancing computational methods in metallurgical process optimization, particularly in understanding magneto-hydrodynamic behavior during induction melting.Peer reviewe
Reduction of Vision-Based Models for Fall Detection
Get PDFPublisher Copyright: © 2024 by the authors.Due to the limitations that falls have on humans, early detection of these becomes essential to avoid further damage. In many applications, various technologies are used to acquire accurate information from individuals such as wearable sensors, environmental sensors or cameras, but all of these require high computational resources in many cases, delaying the response of the entire system. The complexity of the models used to process the input data and detect these activities makes them almost impossible to complete on devices with limited resources, which are the ones that could offer an immediate response avoiding unnecessary communications between sensors and centralized computing centers. In this work, we chose to reduce the models to detect falls using images as input data. We proceeded to use image sequences as video frames, using data from two open source datasets, and we applied the Sparse Low Rank Method to reduce certain layers of the Convolutional Neural Networks that were the backbone of the models. Additionally, we chose to replace a convolutional block with Long Short Term Memory to consider the latest updates of these data sequences. The results showed that performance was maintained decently while significantly reducing the parameter size of the resulting models.Peer reviewe
Development of a 3D Digital Model of End-of-Service-Life Buildings for Improved Demolition Waste Management through Automated Demolition Waste Audit
Get PDFPublisher Copyright: © 2024 by the authors.This paper presents the development of a 3D digital model of end-of-service-life buildings to facilitate a step change in preparation of pre-demolition protocols that can eliminate problems of inadequate documentation and extensive time spent in preparing pre-demolition audits. The 3D digital model consists of the following four main components: (i) digitization of paper-based drawings and their conversion to CAD; (ii) automated generation of a 3D digital model from CAD; (iii) corrections to the 3D digital model to account for changes in the lifetime of a building; (iv) a sub-model for performing pre-demolition audit. This paper proposes the innovative approaches of incorporating a minimal amount of human intervention to overcome numerous difficulties in automated drawing analysis, application of augmented reality (AR) in corrections to the 3D digital model, and data compatibility for pre-demolition audit. These processes are demonstrated using one building as case study. Using the digital model, a pre-demolition audit can be prepared in minutes rather than the many days required in current practice without a digital model. The accurate quantification of the quantities and locations of different demolition waste materials and products in buildings to be demolished will enable a systematic and quantitative evaluation of potentials of material and product reuse and eliminate contamination of different demolition waste streams (which may contain hazardous waste), which is the main cause of environmental degradation and downcycling of demolition waste materials.Peer reviewe
Scientific advances regarding the effect of carbonated alkaline waste materials on pozzolanic reactivity
Get PDFPublisher Copyright: © 2024 The AuthorsUsing alkaline waste materials as biomass ash (BA), Recycled Concrete Fines (RCF) from constructions and demolition waste (CDW) and ladle furnace slags (LFS), as CO2 massive sinks worldwide through the generation of new potential SCM by Accelerated Carbonation Technologies to reduce the cement industry's carbon footprint is increasingly attracting attention for its environmental benefits and the materials' improved performance in blended cement matrices. This paper employs characterization and identification techniques (XRF, XRD–Rietveld, SEM/EDX, BET, TG/DTG, FTIR, NMR) to analyse the effect of accelerated carbonation of white ladle furnace slag, siliceous construction and demolition waste and biomass ash on those materials' physical and mineralogical properties, their chemical reactivity and their pozzolan/lime systems' mineralogical phases. The results show that although behaviour differs depending on the carbonated waste materials' alkalinity, all three present notable increases in BET surface area (7–17.5 m2/g) and substantially altered potentially carbonatable mineralogical phases (e.g. portlandite, Ca-olivine, periclase, hydrated cement phases), which mostly transition towards calcite, due to the CO2 uptake. Thermodynamic modelling of the pozzolanic reaction indicates that CSH/C(A)SH) gels are the most stable phases, followed by ettringite, C4AH13 and C4AcH12.Peer reviewe
Feasibility Study of Processing P91 Steel Alloy via Binder Jetting for Energy Sector Applications
No full textPublisher Copyright: © European Powder Metallurgy Association (EPMA)In response to the recent demand for innovation in new sustainable energy sources, nuclear fusion emerges as a highly relevant and significant process. Given the international effort invested in manufacturing functional reactors, Additive Manufacturing (AM) stands out as a technology that can contribute to meeting the challenges and objectives of applications requiring advanced designs. This study explores the manufacturability of P91 alloy using Binder Jetting (BJ) for high-pressure applications, including those within prospective fusion reactor systems. Following the optimization of the AM process, subsequent adjustments in sintering, Hot Isostatic Pressing (HIP), and various required heat treatments have resulted in excellent material quality in terms of microstructure. Therefore, this study validates the successful use of BJ technology for employing P91 alloy.Peer reviewe
