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A machine-learning based approach for multi-scale optimisation of heat exchangers with lattice-like topology
Get PDFIn this study, we present a novel approach for the multi-scale simulation and optimisation of Micro Channel Heat Exchangers (MCHX) with lattice-like topology. By treating the Triply Periodic Minimal Surface (TPMS) lattice as an equivalent porous medium, the model enables fast and accurate simulations of full-scale 3D MCHX for industrial applications without requiring high-resolution meshing or computationally demanding high-fidelity CFD runs. Micro- and meso-scale effects are accounted for thanks to variable permeability, Forchheimer, and heat transfer coefficients, modelled as non-linear functions of local flow conditions and lattice geometry. These closure relationships are inferred using a multi-fidelity machine learning model, trained on a combination of low- and high-fidelity CFD data. This allows the model to capture the effects of fluid flow phenomena occurring at the smallest scale (such as boundary effects and head pressure losses) without the need to rely on high-fidelity simulations. The presented framework offers a favourable balance between accuracy and cost, enabling optimisation within realistic industrial timelines. As a demonstration, the proposed methodology is applied to the optimisation of a heat exchanger used by Rolls-Royce Plc for the thermal management of high-power electronics in aeronautical applications. In particular, three representative configurations are extracted from the Pareto front, respectively optimised for maximum heat transfer, minimum pressure drop, and a balanced trade-off, thus demonstrating the flexibility of the proposed method in targeting different design priorities
URDICO - Urban Dimension of Cohesion Policy and other EU Programmes: Policy Recommendations
No full textThis annex of the ESPON URDICO final report presents policy recommendations to strengthen the urban dimension of EU Cohesion Policy beyond 2027. Drawing on city case studies, multi-level analysis, and participatory consultations, it addresses challenges such as fragmented governance, limited administrative capacity, and weak alignment between urban strategies and EU funding. Recommendations guide cities, regions, national authorities, and the EU on improving strategic roles, multi-level coordination, integrated territorial approaches, and direct urban engagement in policy design and fund management. The brief provides practical, evidence-based guidance to enhance cities’ ability to deliver sustainable, inclusive, and strategically aligned EU investments
Representation Across Boundaries: New Paradigms in the Age of AI and XR
No full textThe introduction and massive expansion of new algorithms based on Machine Learning (ML) and Deep Learning (DL) processes to support knowledge and design processes is revolutionising multiple knowledge domains in recent years. Among them, research in the fields of Cultural Heritage, Design and Architecture is fueling the introduction of new methodologies of study and content creation, partly in support of existing tools and partly as a complete replacement for them, offering a new paradigmatic view of the impact of AI in these domains. Specifically, the introduction of GenAI and the construction of new content within the three domains opens new avenues in the understanding, analysis, design, and communication of both architecture and design, while highlighting limitations and risks in their unknowing use and opening up ethical questions. Human support and supervision in genera-tive processes is still, fortunately, a foundational aspect of the processes, providing control over the results, stimulating their implementation in different areas. Through a concise review of current research in the field, the article provides an up-to-date frame of the latest global research, foreshadowing potential developments in the immediate future of XR and AI in Cultural Heritage, Design, and Architecture
Design and Experimental Validation of a 12 GHz High-Gain 4 × 4 Patch Antenna Array for S21 Phase-Based Vital Signs Monitoring
Get PDFNon-contact monitoring of human vital signs using microwave radar has attracted increasing attention due to its capability to operate unobtrusively and through clothing or light obstacles. In vector network analyzer (VNA)-based radar systems, vital signs can be extracted from phase variations in the forward transmission coefficient S21, whose sensitivity strongly depends on the electromagnetic performance of the antenna system. This work presents the design, optimization, fabrication, and experimental validation of a high-gain 12 GHz 4 × 4 microstrip patch antenna array specifically developed for phase-based vital signs monitoring. The antenna array was progressively optimized through coaxial feeding, slot-based impedance control, stepped transmission line matching, and mitered bends, achieving a simulated gain of 17.8 dBi, a measured gain of 17.06 dBi, a reflection coefficient of −26 dB at 12 GHz, and a total efficiency close to 74%. The antenna performance was experimentally validated in an anechoic chamber and subsequently integrated into a continuous-wave VNA-based radar system. Comparative measurements were conducted against a commercial biconical antenna, a single patch radiator, and an MIMO antenna under identical conditions. Results demonstrate that while respiration can be detected with moderate-gain antennas, reliable heartbeat detection requires high-gain, narrow-beam antennas to enhance phase sensitivity and suppress environmental clutter. The proposed array significantly improves pulse detectability in the (1–1.5) Hz band without relying on advanced signal processing. These findings highlight the critical role of antenna design in S21-based biomedical radar systems and provide practical design guidelines for high-sensitivity non-contact vital signs monitoring
Digital VoC analysis for product/service quality tracking in the era of Quality 4.0
Get PDFPurpose – This study aims to explore how organizations can leverage digital voice-of-customer (VoC) data to
effectively monitor and enhance the quality of products and services. Specifically, it investigatesthe application
of the KA (key attribute) VoC Map, a novel analytical framework designed to systematically extract insights
from digital customer feedback, categorize key product and service attributes and support continuous quality
improvement in line with Quality 4.0 principles.
Design/methodology/approach – The KA-VoC Map leveragestopic modeling algorithmsto analyze customer
feedback from digital platforms, identifying key attributes and categorizing them based on their frequency of
discussion (mean topical prevalence) and associated sentiment (mean rating proportion). A case study involving
smartwatch feedback collected from 2021 to 2024 demonstrates the practical implementation of the
methodology.
Findings – The results reveal the utility of the KA-VoC Map in identifying and prioritizing key quality
attributes, monitoring their evolution over time, and supporting continuous quality improvement.
Originality/value – This study introduces a novel methodological enhancement of the KA-VoC Map,
demonstrating its use for dynamic quality tracking over time. This approach enables continuous monitoring of
customer sentiment evolution, providing actionable insights for proactive quality management in the era of
Quality 4.0
Spring trends in slow-moving landslide displacement: is it a reliable way to predict their movements? Two case studies in the Susa Valley (NW Italy)
Get PDFMountainous regions are highly susceptible to ground instabilities due to their geomorphological features and the climate events. Slow-moving landslides are influenced by multiple interacting predisposing factors, complicating the prediction of acceleration patterns. Rising groundwater levels, closely linked to net precipitation and snowmelt, are the main drivers of slope movements. Thus, reconstructing ground instability scenarios requires analysing groundwater dynamics. In this context, the present study focuses on the methodological development and validation of an alternative approach for landslide monitoring, rather than on the direct cause-effect relationship between groundwater variation and slope movements. The proposed method aims to assess the reliability of using spring water levels as a proxy for predicting slope displacements, and to compare this approach with the use of piezometric data, which are typically employed in conventional monitoring system. This is particularly relevant in mountainous environments, where the number of in situ instruments is often limited. The methodology combines statistical tools and Fourier spectral analysis to investigate the coherence between hydrogeological and kinematic time series. The analysis was applied to two large slow-moving landslides in the Western Italian Alps (Champlas du Col and Thures). Results show that when springs and inclinometers are in proximity and belong to the same landslide dynamics, as in Thures landslide, the spring trend can effectively predict conditions triggering movement. Conversely, at Champlas du Col landslide, discrepancy between spring and displacement trends suggests the presence of a sub-landslide within the main body. This is likely related to local geological settings, as confirmed by the strong correlation between piezometric data and displacement rates. Overall, this research highlights the methodological potential of integrating spring monitoring into landslide observation frameworks, providing a cost-effective and scalable tool for areas with limited instrumentation. This approach lays the groundwork for the development of generalised criteria to identify suitable springs and to support the forecasting of slope accelerations in similar geological contexts
A Multi-scalar and Multi-modal Approach to Architectural Heritage Documentation: An Interactive Digital Representation of the St. Nicholas Chapel
No full textThe documentation, curation, and dissemination of endangered architectural heritage requires multidisciplinary expertise and imaginative engagements with heterogeneous formats and scales of analysis. Through a case study of a Carpathian-style chapel in Beaver, Pennsylvania, St. Nicholas Chapel, we explore how an architectural heritage building might be interactively documented in a way that situates it within long-standing architectural traditions while remaining attentive to its social and material specificity. We present a prototype of an interactive document showcasing a multi-modal and multi-scalar approach to architectural heritage that innovatively brings together data-intensive typological analyses, historical documentation, ethnographic interviews, geometric studies, and photogrammetry and LiDAR captures. The web-based document offers a highly detailed portrait of the St. Nicholas Chapel, inviting a broad audience of users to dynamically explore it through a combination of high-quality imaging and scanning methodologies, artificial intelligence (AI)-enabled geometric analyses, historical materials, and first-person accounts of its construction
On-orbit refueling robust mission scheduling with uncertain duration for geosynchronous orbit spacecraft
Get PDFWith the increasing number of geosynchronous orbit satellites with expiring lifetime, spacecraft refueling is crucial in enhancing the economic benefits of on-orbit services. The existing studies tend to be based on predetermined refueling duration; however, the precise mission scheduling solution will be difficult to apply due to uncertain refueling duration caused by orbital transfer deviations and stochastic actuator faults during actual on-orbit service. Therefore, this paper proposes a robust mission scheduling strategy for geosynchronous orbit spacecraft on-orbit refueling missions with uncertain refueling duration. Firstly, a robust mission scheduling model is constructed by introducing the budget uncertainty set to describe the uncertain refueling duration. Secondly, a hybrid Harris-Hawks optimization algorithm is designed to explore the optimal mission allocation and refueling sequences, which combines cubic chaotic mapping to initialize the population, and the crossover in the genetic algorithm is introduced to enhance global convergence. Finally, the typical simulation examples are constructed with real-mission scenarios in three aspects to analyze: performance comparisons with various algorithms; robustness analyses via comparisons of different on-orbit refueling durations; investigations into the impacts of different initial population strategies on algorithm performance, demonstrating the proposed mission scheduling framework's robustness and effectiveness by comparing it with the exact mission scheduling
AI-Driven Adaptive Photogrammetry for Built Heritage Information Modelling
No full textThe optimisation of massive data obtained from 3D acquisition methodologies through AI represents an innovative research frontier in 3D data management. It arises from the ever-increasing instruments’ capacity to acquire enormous amounts of geometric and radiometric information with a substantial increase in processing times, a demand for computing capacities, and the request to subsample ultra-dense point clouds at the end of the process. On the contrary, a priori intervention on the raw data can mitigate the role of data dimension, reducing processing times while preserving the valuable information to analyse and interpret the artefacts. The research presents a new methodological approach based on integrating photogrammetry and AI. Through AI algorithms, it was possible to optimise the weight of the images, automatically cluster and segment image areas, and assign different resolutions according to the image content. This experimental pipeline significantly reduced calculation times, extracted point clouds with variable resolution according to the elements represented, and preserved the architectural artefacts’ geometry
Tailoring the Shape‐Memory Performance of 2D and 3D Fabricated Semi‐Crystalline PCL Networks Via Optimal Crosslinking
No full textPhoto-crosslinking is a fast and efficient approach to obtain chemically crosslinked semi-crystalline networks featuring both one-way and two-way shape-memory effect. However, the effect of photo-crosslinking parameters and fabrication method on the physical, thermo-mechanical, and shape-memory properties of these networks still has to be investigated. This paper aims to fill this gap, specifically focusing on semi-crystalline polycaprolactone (PCL) networks. In detail, the influence of key photo-crosslinking parameters -crosslinking temperature and UV light intensity- as well as the fabrication method -2D vs. 3D- were investigated. As a general trend, crosslinking above the melting temperature of PCL and selecting a high UV light intensity yielded structures with superior performance, also displaying stress-free shape-memory behavior. Conversely, crosslinking below the crystallization temperature of PCL and selecting a low UV light intensity led to reduced performance and absence of stress-free actuation. To address this limitation, a post-treatment involving additional UV exposure was introduced, which significantly improved overall performance, particularly enhancing the two-way shape-memory behavior. Interestingly, although the 3D printed samples displayed thermal properties comparable to their 2D counterparts, their shape-memory performance was significantly reduced. Overall, these findings provide practical design guidelines for engineering 2D and 3D PCL-based semi-crystalline structures with tunable physical, thermal, and shape-memory properties