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Impact of phase change material-based thermal management on battery thermal safety
No full textThe increasing demand for electric vehicles (EVs) has intensified research on lithium-ion battery safety, particularly regarding thermal runaway (TR) and thermal management systems (TMS). This review battery safety, explores the mechanisms of TR, including mechanical, electrical, and thermal abuse, and highlights strategies for mitigating TR through effective battery thermal management (BTM). Phase change materials (PCMs) have emerged as promising passive thermal solutions due to their latent heat storage capabilities; however, their low thermal conductivity and leakage issues present significant challenges. Recent advancements in composite PCMs, incorporating nanomaterials such as expanded graphite, boron nitride, and metal oxides,
have significantly improved heat dissipation and stability. Additionally, active cooling methods, including air, liquid, and thermoelectric systems, are evaluated in hybrid approaches that enhance battery safety and performance. Integrating flame-retardant additives and encapsulated PCMs further improves thermal stability and fire resistance. Artificial intelligence (AI) driven material development strategies are also proposed to optimize PCM formulations and real-time BTM assistance. This review provides a comprehensive analysis of current BTM techniques and future research directions, emphasizing the role of nanotechnology and hybrid cooling methods in enhancing EVs' battery performance, safety, and longevity
Real-time HiL Model of Variable Flux Synchronous Motor Drives
No full textVariable Flux Machines (VFMs) employ low coercivity permanent magnets that allow intentional demagnetization and remagnetization to optimize drive performance. Accurate modeling of their nonlinear magnetic behavior, including irreversible demagnetization and load dependent asymmetric flux linkage, typically relies on computationally intensive Finite Element Analysis (FEA), which is unsuitable for advanced control development and real-time applications. This paper proposes a simplified VFM model that captures the essential magnetic phenomena without requiring FEA. The model significantly reduces computational effort while maintaining high accuracy. Its efficiency enables real-time Hardware in the Loop (HiL) implementation on a PLEXIM RT Box 2 with control executed on an STM32G4 microcontroller. Validation against FEA demonstrates strong agreement and substantial computation time reduction, making the proposed approach suitable for real-time validation and control development of VFMs
Artificial Intelligence in Minimally Invasive and Robotic Gastrointestinal Surgery: Major Applications and Recent Advances
No full textArtificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice
Linking Social Justice with Climate Justice at the Coal Mines Historic Site in Tasmania (Australia)
No full textThis chapter explores the current conservation and management challenges at the Coal Mines Historic Site, which is a component of the UNESCO World Heritage Listed Australian Convict Sites. Based on the preparatory work of the ICCROM-IUCN World Heritage Place Lab which aimed to create better connections between site managers and scholars to effectively implement the World Heritage Convention, the chapter argues that holistic management and conservation of heritage sites and landscapes, particularly those with deep colonial legacies, can only be achieved through an understanding of the interrelatedness of issues of social justice and climate justice
Leveraging quantum computing for heat conduction analysis: A case study in thermal engineering
No full textReal-time event-based particle image velocimetry for active flow control
Get PDFThis work investigates event-based vision (EBV) as a tool for real-time flow diagnostics in configurations analogous to two-dimensional, two-component particle image velocimetry. Owing to its reduced data stream compared to conventional frame-based imaging, EBV enables kilohertz-rate pseudo-framing and efficient processing on standard computing hardware. A pseudo-frame-based implementation called real-time event-based imaging velocimetry is presented, capable of delivering velocity fields at several hundred hertz with O(10^6) vectors per second.
The concept is experimentally demonstrated on a small-scale jet in water, where event rates above 100×10^6 events/s and online processing at 250−700 Hz are achieved depending on seeding and interrogation settings. Beyond these validation cases, two active flow control applications on a jet in air are illustrated: open-loop optimization of jet mixing using Bayesian optimization, and closed-loop control of a water jet using reinforcement learning. These results highlight EBV as a cost-effective and scalable sensing technology with strong potential for real-time feedback in flow control
Inference of the size of nonlinear network systems from perceptible dynamics
No full textNetwork dynamical systems are ubiquitous in science and engineering. The most basic property of a network dynamical system is its size, which, for scalar dynamics, corresponds to the number of nodes. For linear network systems, recent studies have developed reliable tools for inferring the size of the system from perceptible dynamics (measurements of one or some of the network nodes) across multiple experiments. Here, we extend these tools to nonlinear network systems by putting forward a model-agnostic approach that combines clustering techniques, the use of detection matrices, and spectral analysis. The theoretical premise of the algorithm is that, under mild assumptions, the variation between the dynamics of some nodes across multiple measurements can be used to bound the variation between the dynamics of all nodes across the same measurements. By applying clustering techniques on perceptible dynamics, we identify nearby measurements, about which the variational dynamics are approximately linear and the use of the detection matrix is valid. From the spectrum of the detection matrix, we infer its rank, which corresponds to the size of the nonlinear network system. We demonstrate our approach via numerical experiments on different nonlinear network systems, including different types of hypergraphs. Whether nonlinearity comes from individual dynamics of the nodes or the interactions among them, it is rarely a feature that one can dismiss. Our work paves the way to infer the size of a nonlinear network system when governing equations are unknown and only limited data are accessible
On the formation of strange quark stars from supernova in compact binaries
No full textStrange quark stars (SQSs), namely compact stars entirely composed of deconfined quark matter, are characterized by similar masses and compactness to neutron stars (NSs) and have been theoretically proposed to exist in the Universe since the 1970s. However, multiwavelength observations of compact stars in the last 50 years have not yet led to an unambiguous SQS identification. This article explores whether SQSs could form in the supernova (SN) explosion of an evolved star (e.g., carbon-oxygen, or Wolf-Rayet) occurring in a binary with the companion being a neutron star (NS). The collapse of the iron core of the evolved star generates a newborn NS and the SN explosion. Part of the ejected matter accretes onto the NS companion as well as onto the newborn NS via matter fallback. The accretion occurs at hypercritical (highly super-Eddington) rates, transferring mass and angular momentum to the stars. We present numerical simulations of this scenario and demonstrate that the density increase in the NS interiors during the accretion process may induce quark matter deconfinement, suggesting the possibility of SQS formation. We discuss the astrophysical conditions under which such a transition may occur and possible consequences
Digital Optical Performance Monitoring of Coherent Long-Haul and Metropolitan Optical Fiber Links
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
A genetic algorithm to optimize the multi-group structure for the neutronic analyses of the ARC fusion reactor
No full textIn the framework of the modeling of fusion reactors with deterministic neutronic codes, the choice of an appropriate energy grid for the generation of the multigroup nuclear properties is essential. In this work, a Genetic Algorithm is employed to optimize the energy grid employed in the nemoFoam multiphysics code to reproduce the results provided by the Monte Carlo code Serpent in terms of neutron flux, neutron power deposition and Tritium Breeding Ratio for the Affordable, Robust and Compact (ARC) fusion reactor. Different runs of the Genetic Algorithm are performed, with the aim of optimizing not only the quantities of interest separately, but also trying to combine them thanks to the definition of appropriate fitness functions. The optimization is performed starting from a pre-defined 86 groups energy grid, over which the nuclear properties and the reference quantities are evaluated with Serpent. The results show that it is not straightforward to optimize at the same time the energy grid for different quantities and that, in general, coarse energy grids are able to provide good results in nemoFoam for what concerns the ARC reactor, allowing to alleviate the computational burden of the neutronic evaluation too