Abstract

Abstract

Abstract

As the demand of the automotive and aeronautics industry for high-performance structures increases, the tendency goes towards the use of composite materials. This tendency requires designing efficient manufacturing processes to ensure repeatability and quality. Attending those requirements, the Automated Tape Laying process has gained relevance, as an out-of-autoclave process, consuming less energy and lower production times. This process consists of continuously heating a thermoplastic-based composite material and, using a compaction roll, pressing it against a mould, consolidating the structure by cooling down the composite. On the other hand, the Automated Tape Laying process arises a relevant problem related to the control system. The problem is related to the impossibility of measuring the temperature at the nip point, the temperature under the compaction roll, having no feedback then, no control decision. To overcome this type of problem, some authors have implemented a model-based control system, which allows to estimate the temperature at the desired location. To guarantee the best estimation, the model must be as detailed as possible, which leads to the concept of a Digital Twin. The Digital Twin as a detailed mathematical representation of the physical world, requires knowing the behaviour of the process element due to their interaction. This work proposes a thermal characterization methodology, using an in-house designed method, to describe the thermal-optical and thermal-conductivity properties of composite materials as a function of their temperature, which is required for the Digital Twin to compute the temperature distribution along an Automated Tape Laying process to lower the uncertainties due to properties approximations

Abstract

This work introduces an innovative Structural Health Monitoring (SHM) solution for offshore wind platforms, featuring an advanced Digital Twin (DT) built on a fully-coupled aero-servo-hydro-elastic model. Our approach utilizes a detailed Finite Element model of the structure, meeting the requirements of the main assessment/certification standards. The application of the unique Enriched Modal Matrix Reduction technique leads substantially reduces CPU time, enabling near real-time calculations without compromising accuracy compared to the original FE model.

The SHM system is completed with an optimized sensors setup to monitor the most relevant deformation modes. Additionally, it enables precise fine-tuning of the DT model using machine learning, resulting in an accurate Hybrid Analysis Model.

Our modular and flexible DT-based SHM solution can be customized for any offshore wind platform concept, covering substructure, towers, mooring, and umbilicals. The solution is demonstrated through sea trials on Enerocean’s W2Power prototype.

Abstract

Cultural heritage (CH) assets are the legacy of a society that are inherited from the past generations and can give us lessons for contemporary construction. Not only the formally recognized CH assets but also the non-CH structures and infrastructure, and the interconnection between them are crucial to be considered in a vulnerability assessment tool for the sustainable reconstruction of historic areas. Since most CH assets were not designed based on robust design codes to resist natural hazards such as earthquakes, vulnerability assessment and preservation are pivotal tasks for the authorities. For this aim, Hyperion, an H2020 project (Grant agreement No 821054), was formed in order to take advantage of existing tools and services together with novel technologies to deliver an integrated vulnerability assessment platform for improving the resiliency of historic areas. Geometric documentation is the first and most important step toward the generation of digital twins of CH assets that can be facilitated using 3D laser scanners or drone imaging. Afterward, the finite element method is an accurate approach for developing the simulation-based digital twins of cultural heritage assets. For calibration of the models, the result of the operational modal analysis from the ambient vibration testing using accelerometers can be utilized. Structural analysis for the prediction of the structural behavior or near real-time analysis can be carried out on the calibrated models. However, the full finite element analysis needs a lot of computational effort, and to tackle this limitation, equivalent frame methods can be utilized.

Abstract

The paper presents the results of an ongoing research activity dealing with Digital Twin (DT) approaches for Intelligent Construction, optimizing Operation and Maintenance of buildings and urban areas. DT is an essential technological node of Industry 5.0, which leads to the growth of a Collaborative Industry based on the cooperation between machines and human beings to give added value to processes that meet the needs of users. DT realizes the synchronization between two realities: physical objects in real space, virtual objects in virtual space, remaining connected through the mutual exchange of data throughout the entire life cycle of the object. The construction sector is also adopting the new DT perspective to enforce innovative, responsible, and sustainable governance of the life cycle of buildings improving their durability and reducing environmental impacts. Due to IoT, AI, and virtualization, DT approaches permit prediction of future performance in-use, prevent anomalies, downtime, and inefficiencies, to experiment improvements or changes without having to test them on the construction itself or on special mock-ups. The research assumes the aforesaid considerations and states as its principal operational and experimental assets as-built virtual models in OpenBIM connected with Geographic Information Systems and an IoT infrastructure. They realize intelligent constructions integrated with an analytical data communication network for control and performance simulations. Through the study cases analyzed, the paper also introduces arguments for a critical literature review on current statements and future challenges of the DT approach for Intelligent Construction.

Abstract