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Energy efficiency in digital infrastructures: a framework towards a sustainable cloud manufacturing
No full textCloud manufacturing integrates cloud computing technologies with traditional manufacturing processes, improving operational efficiency and flexibility. However, it also increases the demand for energy, especially in data centres, which exacerbates the environmental impact. To address this issue, this research work presents a framework suitable for identifying, characterising, and managing energy inefficiencies in digital infrastructures. The framework includes energy data collection, inefficiency characterisation, root cause analysis and implementation of a targeted solution. By focusing on key factors such as server utilisation, data management and resource allocation, the proposed methodology aims to contribute reducing energy consumption, operational costs and CO2 emissions. The iterative approach proposed in the framework could enable continuous improvement and adaptation, promoting sustainable cloud manufacturing in digital infrastructure environments
Real-time spatiotemporal temperature gradient estimation based on a graph convolutional neural network for battery cells
No full textMonitoring the temperature distribution of cells in large-scale battery packs can be a costly endeavour. This paper proposes a spatiotemporal model based on a graph convolutional neural network for the estimation of the axial surface temperature distribution of 21 700 cylindrical cells in a small-scale battery pack. The model is developed on the basis of experimental results obtained from the studied pack subject to various immersion thermal management conditions. The pack is charged and discharged at current rates of 0.5C and 1C at four immersion ratios: 25%, 50%, 75% and 100% with respect to the total cell height. A cross-fold training methodology is employed to train the model making use of thermally dynamic input data. The results show that a root mean square error of less than 1.1 °C can be achieved for the fold displaying the poorest performance. Finally, this paper includes a processor-in-the-loop analysis, indicating the feasibility of the model to run on embedded hardware. It is shown that up to 50 cells may be monitored in real-time. The model offers the possibility to expand the safety and health monitoring in battery management systems for not only battery packs with novel thermal management solutions, but conventional solutions as well
Chiral geometric-phase metasurface for Bloch surface wave out-coupling in free space
No full textVortex beams (VBs) represent an important instance of structured light carrying orbital angular
momentum (OAM), which is attracting particular interest from many application fields, from free space optical
communications to sensing and imaging. As OAM radiation from a single emitter is concerned, a precise
positioning of the source in the center of axis-symmetric, possibly resonating diffractive structures is often
required. In addition, efficient free-space outcoupling of light with pure OAM and polarization states remains a
difficult task. Here, we propose a dielectric multilayer platform decorated with chiral gold metasurfaces able to
provide polarization-selective diffraction of Bloch surface waves sustained by the multilayer. We demonstrate
that the generation of well-defined OAM occurs only when Bloch surface waves (BSWs) are coupled (in this
case, from an external coherent source). The metasurface chirality is shown to be polarization-selective so
that at least 74% is out-coupled in free space with a specific circular polarization, determined by design.
In perspective, this work suggests new opportunities for the generation of free-space OAM single photons as
an alternative to plasmonic structures recently proposed
A probabilistic framework for the resilience assessment of transport infrastructure systems via structural health monitoring and control based on a cost function approach
Get PDFThe essential role of transport infrastructure systems for economic development, territorial cohesion and social transformation is widely recognized. However, key structural components of this systems, such as bridges, are rapidly aging, while the loading conditions to which they are subjected are evolving to become increasingly severe, for instance, due to changes in vehicle loads, climate crisis, etc. These circumstances contribute to reduce the level of reliability and safety of these vital infrastructure systems. Therefore, assessing the current state and predicting the future condition of transportation infrastructure, and protecting it against external hazards, proves essential. This paper focuses on an in-depth study of the role of structural control and monitoring in improving the structural resilience of transportation infrastructure as a life-cycle indicator. Subsequently, a novel framework based on a cost function approach is introduced, recognizing that the benefits of enhanced resilience come with associated investments. This enables stakeholders to assess the balance between initial investments and long-term gains, facilitating informed decisions on control and monitoring to ensure structural resilience
A metrological approach for Augmented Reality tooltip tracking assessment
Get PDFTracking systems are essential in various fields, such as health and manufacturing industries, enabling mapping
between the real and digital worlds. Amongst others, Augmented Reality Tracking Systems (ARTS) are more
recent and less explored. This work proposes a quantitative metrological methodology to evaluate ARTS tooltip
tracking performance, facilitating benchmarking, parameter optimization, and system selection for specific
tasks. A specific 3D-printed measuring artifact is proposed to guide tooltip positioning. Tracking accuracy
and precision are estimated, highlighting the effects of influence factors. The methodology was tested with
two commercial state-of-the-art ARTSs using marker-based tooltips, i.e., a Microsoft HoloLens 2 and a stereo
camera system equipped with Intel RealSense SR305 cameras. Metrological characteristics are evaluated, and
the Euclidean distance expanded uncertainty at a conventional 95% confidence level is estimated as 5.071 mm
for the HoloLens 2 and 6.800 mm for the stereo system, resulting in a superior metrological performance
of HoloLens 2 under the specified conditions. This study provides a standardized approach for quantitatively
comparing AR tracking systems, offering valuable insights for optimizing their use in specific applications and,
innovatively in the context of ARTS, associates measurement uncertainty with tracked distance values
Masking effect of surface coatings in long pulsed thermography: A quantitative analysis of thickness discrimination
Get PDFActive thermography is a powerful non-destructive evaluation (NDE) technique for characterizing materials and detecting defects. A common practice to enhance measurement reliability is the application of a high-emissivity paint coating. However, this coating can introduce a thermal barrier, potentially masking the intrinsic thermal response of the underlying substrate, which is critical for tasks such as thickness discrimination. This study provides a quantitative analysis of the masking effect induced by a black paint coating on the transient thermal response of steel samples of varying thicknesses, as measured by laser-long pulse active thermography. We employ a combined approach of analytical modeling, numerical simulation, and experimental validation. The analysis is grounded in the one-dimensional heat conduction equation, with a lumped-parameter model used to explore the system’s primary dynamics. Experimental tests on a multi-thickness steel block provide validation data. Our findings reveal that the low thermal diffusivity of the paint layer creates a significant initial thermal transient that can obscure the thickness-dependent response of the substrate. This masking effect is particularly pronounced for thicker coatings and substrates. We demonstrate that spatial averaging of the thermal data can partially mitigate this effect, restoring thickness discrimination for substrates up to a certain thickness threshold. The study concludes that while emissivity-enhancing coatings are beneficial, their thermal properties must be carefully considered in the design and interpretation of long pulsed thermography experiments. We propose practical guidelines for optimizing coating selection and data analysis strategies to preserve the integrity of NDE measurements
Enhancing Public ‘Awaiting Reuse’ Architectural Heritage through RECs: the Former Ammunition Depot in Sangano
Get PDFThe research investigates the potential of economic evaluation as a strategic lever for enhancing architectural and landscape heritage in fragile contexts, adopting a life-cycle perspective and integrating it into sustainable regeneration processes promoted by Renewable Energy Communities (RECs). It is assumed that the energy transition can represent an opportunity for local administrations to initiate collaborative governance processes aimed at territorial revitalization, particularly in marginal areas. This study proposes a methodological approach to economically evaluate energy empowerment interventions on underutilized historic buildings and cultural sites awaiting reactivation. At the core of the approach lies Life Cycle Cost Analysis (LCCA), employed as a decision-support tool to guide long-term economically sustainable public policies. The methodology has been applied to the Former Ammunition Depot of Sangano (Turin), a decommissioned military site under municipal ownership, lacking specific heritage protection and located within a fragile territorial context with limited resources. Particular attention was devoted to the reuse of public spaces and the design of lighting scenarios, conceived both for the functional and symbolic renovation and enhancement of the site. The economic evaluation, conducted through LCCA, simulated the integration of the complex into a local REC to verify the capacity of energy-sharing models to offset the costs of adaptive reuse, with a specific focus on public space lighting. The direct involvement of the Municipality demonstrated how analytical tools such as LCCA can support multi-level decision-making and foster the transformation of dormant heritage assets into strategic resources for local development
Toward Correctness by Construction for Network Security Configuration
No full textCyber attacks have been continuously becoming more frequent and powerful in modern computer networks, which interconnect a huge number of devices, including mobile ones. A main reason explaining the effectiveness of the attacks lies in the errors introduced by human administrators in the configuration of network security functions such as firewalls and VPN gateways. In literature, formal methods were used to check if a security configuration satisfies the policies describing the security properties to be enforced in the network. However, most existing proposals use a-posteriori formal verification, which still requires multiple tasks to be carried out by human administrators and may be time-consuming. In order to overcome these limitations, we have carried out research toward applying correctness-by-construction approaches for network security configuration. In this paper, we survey our most relevant contributions to this area, describing techniques based on constraint programming, like MaxSMT formulations, for a formal representation and resolution of the automatic security configuration problem, in such a way that the computed result is already proved to be correct and compliant with the requested policies
A deep learning model to predict GNSS from InSAR data
No full textAccurate monitoring of ground deformation is crucial for hazard mitigation, infrastructure management, and environmental protection. Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) are two complementary geospatial technologies whose integration relies predominantly on physical modeling and geometric transformations for fusion. This paper introduces a novel deep learning model that predicts GNSS-like three-dimensional ground displacements at InSAR measurement locations, using weak supervision from spatially sparse GNSS data. Our approach leverages a Dynamic Graph Convolutional Neural Network (DGCNN) backbone to model spatial dependencies among localized InSAR-derived features, effectively calibrating InSAR measurements to correct for viewing geometry limitations. The proposed method is evaluated in an area in the Netherlands affected by induced seismicity and ground subsidence across different experimental scenarios, with a particular focus on predicting ground deformations in time windows not experienced at training tim
Paesaggi della memoria: il caso ThyssenKrupp a Torino
Get PDFThe contribution deals with the exemplary case of one of Italy's most important factories, ThyssenKrupp in Turin. Founded as Fiat Ferriere between 1957 and 1962 on a bend of the Dora River, it now stands abandoned and linked to the memory of the tragedy that took place in December 2007, when seven workers lost their lives in a fire.
The events surrounding ThyssenKrupp appear emblematic of Turin's industrial history and its most recent urban regeneration policies, alternating between moments propulsive for hypothetical transformation and periods of rethinking the same purposes of use on a territorial scale. The problem in promoting proactive actions is linked to the private ownership of the plant and its size, the necessary reclamation work, the challenging access to the area, and the difficult socio-economic context of the Lucento neighborhood.
In particular, our contribution aims to reflect on the documentary and testamentary value of this massive, barrier-like building, which embodies historical significance, collective memory, and a hoped-for memorialization, because the ThyssenKrupp pyre remains “a wound that will not heal” and for the families a story of justice betrayed