Higher Institute on Territorial Systems for Innovation
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Fast and relaxed vector fitting: an application to a fighter jet aircraft
Full text linkThe Fast and Relaxed Vector Fitting (FRVF) is a dynamic System Identification technique for inputoutput measurement, recently proposed for applications in the fields of Aerospace, Civil, and Mechanical Engineering. Here, its capabilities are validated, for the first time, on experimental data from
Ground Vibration Testing (GVT) under controlled laboratory conditions. More specifically, the case
study of interest is a real-size, real-life decommissioned F-16 fighter jet aircraft, widely studied in the
recent literature. The results of these tests are benchmarked against the identifications available in the
published literature, showing very good comparability with different kinds of excitation
The search for a weak legacy. Drawing as a tool to reorient readings of urban space in Tirana
Full text linkThis contribution aims to explore the potential of drawing both as a tool for critical urban analysis and as a basis for transformative scenarios, through some visualisations extrapolated from a research in progress. The work presented is part of an ongoing transnational project (called See:4 Cities) focused on the architectural socialist legacy in the Western Balkans (specifically in: Belgrade, Podgorica, Skopje, Tirane), aiming to rethink it as a partial countermeasure to the real estate speculative drifts that are affecting, albeit in a particular way, the aforementioned cities. Within this framework, a portion of the city of Tirana become here the subject of an in-depth visual analysis based on the construction of a specific syntax: conventional modes of representation are combined to interrelate heterogeneous factors – such as land configuration, tenure regimes, proper and improper uses of the space, social and building density and architectural distribution – thus bringing out material and immaterial elements of the space. Instead of a mere spatialisation of pre-tagged data, such approach primarily aim to reorient questions on particular urban areas and on their transformability. The output, in this sense, is conceived neither as a genealogical mapping nor as a basis for design in the strict sense. Rather, the scope is to identifying relationships which can only be grasped if visualised and thus defining a support for new urban reasoning. As a working method, the meaning of the proposed visualisations is never absolute or definitive: they are incremental, and will evolve further by integrating multiple dimensions depending on the different lens one chooses to adopt. The work deliberately straddles the line between automation and analogic, in the belief that the inquiry phase cannot be delegated in its entirety to a specific software or product, especially if drawing is considered in an epistemological sense
Development and Preliminary In Vivo Study of 3D-Printed Bioactive Glass Scaffolds with Trabecular Architecture
Full text linkThe bioactive silicate glass “1d” (BG-1d), belonging to the multicomponent CaO–MgO–Na2O–P2O5–SiO2–CaF2 system, is proved to be highly effective in promoting alveolar bone regeneration in humans when implanted in the form of particles. This study moves one step forward in developing BG-1d scaffolds with trabecular architecture by digital light processing-based vat photopolymerization to be then implanted in rabbit femora, which has never been reported so far. The scaffolds exhibit a clear osteogenic effect in vivo and undergo gradual resorption followed by ossification. Substantial resorption of scaffolds in favor of new bone formation is achieved within 3 months. These in vivo results support the suitability of BG-1d scaffolds for application in bone tissue engineering and, given the reproducibility and reliability of the 3D-printed devices, show promise for potential translation to the clinic
Research on dynamic characteristics of wind turbine hybrid towers under environmental influences based on operational modal identification and refined finite element simulation
Full text linkAs hybrid towers of wind turbines have gradually become the mainstream structural configuration for high-power wind turbines, their structural safety and long-term reliability have increasingly become critical issues. However, existing studies have insufficiently focused on the impacts of environmental factors and local structural characteristics on hybrid tower structures. To address this gap, this study investigates the dynamic characteristics of wind turbine hybrid towers based on Operational Modal Analysis (OMA) and finite element simulation.
This study proposes a systematic operational modal analysis process. Compared with traditional modal identification methods, the proposed approach combines the Frequency Domain Decomposition (FDD) method and the Stochastic Subspace Identification–Covariance (SSI-COV) method to achieve effective extraction of structural modal frequencies and mode shapes under operational conditions. Through a progressive strategy involving preliminary identification, accurate estimation, and spurious mode elimination, the efficiency and accuracy of modal parameter identification are significantly improved.
The analysis results show that, within the range of conventional environmental variations, no significant statistical correlation is observed between the identified modal parameters (frequency and mode shape) and environmental factors, including wind direction, wind speed, and temperature. It should be noted that the validity of these conclusions is limited to the specific environmental conditions and data acquisition periods investigated in this study—namely, wind speeds of 2.0–5.5 m/s, temperatures of 17 °C–35.8 °C, wind direction variations of approximately 10°, and data collected daily between 11:45 and 12:15—and does not encompass seasonal-scale extreme environmental variations.
Meanwhile, a finite element model of the wind turbine hybrid tower is established and its effectiveness is verified using on-site measured data. Parametric analysis and damage simulation based on the validated model reveal that the elastic modulus of concrete is the most sensitive parameter affecting the overall dynamic characteristics of the structure, compared with the elastic moduli of steel and the foundation. Additionally, the influence of stiffness degradation in different regions of the concrete tower on the natural frequencies of each order is related to the specific location of damage and the modal order of structural vibration.
The conclusions of this study provide a theoretical basis for the optimal design of wind turbine hybrid tower structures and offer important references for formulating structural health monitoring strategies based on dynamic characteristics
Evaluation of N2 and CO2 pyrolyzed biochars in remediation studies: Adsorption and immobilization of potentially toxic elements in water and contaminated soil, and the impact on soil properties
Full text linkUsing CO2 medium (instead of inert gas) during pyrolysis has been reported to improve biochar properties, particularly textural and surface properties; however, its investigation in remediation applications has received little attention. In this study, biochar prepared from rice husk pyrolysis under CO2 atmosphere (BCC) and under N2 atmosphere (BCN) was investigated for efficacy in removing zinc (Zn2+) and lead (Pb2+) in water, immobilizing potentially toxic elements (PTE) in soil, and improving physicochemical properties of a mining soil. Results showed that for aqueous metals adsorption, both biochars followed the pseudo-second order kinetic model. BCC exhibited greater Zn2+ adsorption capacity and efficiency (26 mg/g, 52 %) compared to BCN (22 mg/g, 45 %). In contrast, both biochars had comparable Pb2+ adsorption efficiency (40 %). Furthermore, five-week biochar-soil incubation experiments revealed that, compared to control soil, both biochars significantly improved soil physicochemical properties, such as increasing total and organic carbons, pH, water holding capacity, and cation exchange capacity as the dose increased from 3 to 10 %. However, both biochars showed statistically insignificant differences at similar doses, revealing that soil improvements were strongly dose-dependent rather than biochar type. PTE immobilization experiments revealed that both biochars numerically reduced the bioavailability of all PTEs (Zn, Pb, Cr, Cu, Cd, Ni, Mn) in soil, but the difference was not statistically significant under the tested conditions
Innovations in Transition-Edge Sensors as superconductive single-particle detectors
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
NOx Emission Prediction of a Lean Premixed Hydrogen Combustor via Reactor Network Analysis
No full textHydrogen-fueled gas turbines offer a promising pathway toward zero-carbon aviation, yet the elevated flame temperatures associated with hydrogen combustion promote thermal nitrogen oxide formation. This work develops and validates a chemical reactor network methodology for predicting nitrogen oxide emissions from the AHEAD lean premixed swirl-stabilized hydrogen combustor. The combustion chamber is discretized into five interconnected perfectly stirred reactors representing the principal flow structures. A distinguishing feature of the proposed approach is a volume allocation strategy based on swirl number estimation and geometric analysis, relaxing the requirement for preliminary computational fluid dynamics simulations. Nine network parameters are calibrated via particle swarm optimization algorithm against experimental measurements spanning four equivalence ratios at a fixed inlet air temperature and air mass flow rate. Two chemical mechanisms involving NOx chemistry are evaluated: the Naik mechanism (21 species, 105 reactions) and the CRECK mechanism (31 species, 203 reactions), yielding nearly identical NOx predictions with a mean absolute difference of 2.5 parts per million by volume on a dry basis referenced at 15% O2. The calibrated model demonstrates robust predictive capability across the full experimental matrix without recalibration, spanning inlet air temperatures from 310 K to 700 K, air mass flow rates from 116 kg/h to 255 kg/h and equivalence ratios from 0.30 to 0.96. At optimization points, both mechanisms achieve close agreement with experimental data, while absolute errors for non-reference operating conditions do not exceed 4 ppmv and 8 ppmv for the Naik and CRECK mechanisms, respectively. The methodology establishes a computationally efficient framework for nitrogen oxide prediction in lean premixed hydrogen combustion systems, suitable for preliminary combustor design applications
One Is Enough: Efficient Modeling of RTP Traffic for QoS Predictions in Real-Time Communications
Full text linkIn recent years, we have witnessed an unprecedented upsurge in popularity and advancement of Real-time Transport Protocol (RTP)-based real-time communication (RTC) applications. For the sake of their optimizations, Quality of Service (QoS) prediction serves as a viable venue for enhancing network monitoring and enabling preemptive solutions. However, existing methodologies are typically tailored and constrained to individual traffic flows and QoS metrics, lagging in correlation capturing and computational efficiency. In light of this, we argue that "one model is enough" to conquer these challenges, and propose a novel deep learning (DL) framework namely Oh, employing a teacher-student scheme with two training stages. The first (teacher) involves a sophisticated Long Short-Term Memory (LSTM) neural network (NN) empowered by a customized attention structure, and the second (student) comprises simple feedforward NNs to distill knowledge and reduce complexity. Specifically, Oh leverages a multi-task learning paradigm, mapping extracted features to four key QoS indicators. It is capable of simultaneously handling unlimited amount of concurrent RTP flows with packet-level information and performing end-to-end predictions of multiple QoS metrics in one single shot. Our work is based on massive traffic collected during real video-teleconferencing calls using various software, and benchmarked against multiple other machine learning (ML)/DL algorithms. As a result, Oh-teacher yields superior prediction performance, whereas Oh-student achieves distinctly enhanced temporal efficiency with comparable forecasting outcomes
Joining of Oxide/Oxide Composites by a Preceramic Polymer
Full text linkCeramic matrix composites (CMC), particularly oxide fiber-reinforced oxide matrix (ox/ox)
composites, offer a viable alternative to traditional materials, due to their high temperature thermomechanical stability, intrinsic oxidation resistance and low density compared to metals.
However, a joining material having the same thermo-mechanical behavior and oxidation stability as
the ox/ox composites is still an open issue.
In this respect, joining by a preceramic polymer offers a unique option, in principle enabling the
fabrication of robust ceramic joints; since the process can be done at lower temperatures in respect
to traditional methods such as brazing, it can also be attractive from an energy saving point of view.
This study investigated the use of a polysilazane-based preceramic polymer (Durazane 1800) filled
with an increasing amount of alumina particles and 1 wt % chopped alumina fibers to join and coat
ox/ox composites. The process was the same for joining and coatings: i.e. curing at 180 °C and
pyrolysis up to 1200 °C in air, followed by microstructural and mechanical characterization on each
sample.
Lap-shear tests were done on joined samples at room temperature, at 300 °C and at 600 °C, in air.
Fracture surfaces exhibited cohesive failure, indicating sound adhesion between the joining material
and the ox/ox composites. However, residual porosity and incomplete covering of the joined area
were identified as a limiting factor affecting the joint strength.
X-ray computer tomography (CT) was used to measure the volume of residual porosity, cracks and
lack of coating material after curing and pyrolysis on coated samples
Comparative conflict analysis between autonomous and human-operated vehicles with pedestrians at unsignalized crosswalks
No full textUnsignalized crosswalks remain the most vulnerable scenario where pedestrians are exposed to the highest risks. With the imminent introduction of autonomous vehicles on public roads, safe encounters with pedestrians in these critical environments presents a significant challenge. Our study develops a rigorous methodology to quantitatively assess these dynamics in real-world mixed traffic conditions. We implemented a system that processes video data from on-street cameras to evaluate risks in vehicle-pedestrian interactions by computing key conflict measures, such as the Time-to-Collision (TTC). The analysis conducted at an unsignalized pedestrian crossing enabled a comparative evaluation between conventional and autonomous vehicles. Results highlight a higher incidence of severe conflicts in interactions with human-operated vehicles, suggesting that the cautious programming of autonomous vehicles can significantly contribute to pedestrian safety. Our findings also reveal an impact on the pedestrian decision-making process based on the type of vehicle approaching the crosswalk