Search

Aim of the present manuscript is to provide an overview of all possible methods and light source typologies used by the different research groups for obtaining the energetic nano-materials’ photo-ignition, showing the latest progress related to such phenomenon employing, also, alternative radiation sources to the common Xe lamp. In fact, the employment of a different source typology can open new usage prospects respect to those enabled by the Xe lamp, mainly due to its technological limitations. Therefore, several studies are faced to test light sources, such as lasers and LEDs, for igniting the nano-energetic materials (as CNTs mixed with metallic catalyzers, Al / CuO nano-particles, etc); these nano-materials are usefully employed for starting, in volumetric and controlled way, the combustion of air-fuel mixtures inside internal combustion engines, leading to significant benefits to the combustion process also in terms of efficiency, reliability, and emissions of pollutants. Several research works are presented in literature concerning the ignition of liquid / gaseous fuels, without nano-particles, employing laser sources (i.e laser-based plugs in place of the common spark plugs); therefore, an innovative solution is proposed that employs multi-point laser-plugs for inducing the ignition of nano-materials dispersed into the air-fuel mixture inside the cylinder, so further improving the combustion of the fuel in an internal combustion engine.

The engine fuel control system plays a crucial role in engine performance and fuel economy. Fuel control, in traditional engine control systems, is carried out by means of sensor-based control methods, which correct the fuel flow rate through correlations or scheduled parameters in order to reduce the error between a measured parameter and its desired value. In the presence of component degradation, however, the relationship between the engine measurable parameters and performance may lead to an increase in the control error. In this research, linear models for advanced control systems and for direct fuel control in the presence of components degradation are proposed, with the main objective being to directly predict and correct fuel consumption in the presence of degradation instead of adopting measurable parameters. Two techniques were adopted for model linearization: Small Perturbation and System Identification. Results showed that both models are characterized by high accuracy in predicting the output engine variables, with the mean errors between model prediction and data below 1%. The maximum errors, recorded for shaft power, were about 6% for Small Perturbation and lower than 3% for System Identification. A simple correlation between engine performance and components degradation was also demonstrated; in particular, the achieved results allow one to conclude that the Small Perturbation approach is the best candidate for controller development when a prediction of components degradation is included.

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques.

This study investigates the kinetic modeling of CH4/H2/Air mixture with nanosecond pulse discharge (NSPD) by varying H2/CH4 ratios from 0 to 20% at ambient pressure and temperature. A validated version of the plasma and chemical kinetic mechanisms was used. Two numerical tools, ZDPlasKin and CHEMKIN, were combined to analyze the thermal and kinetic effects of NSPD on flame speed enhancement. The addition of H2 and plasma excitation increased flame speed. The highest improvement (35%) was seen with 20% H2 and 1.2 mJ plasma energy input at ϕ = 1. Without plasma discharge, a 20% H2 blend only improved flame speed by 14% compared to 100% CH4. The study found that lean conditions at low flame temperature resulted in significant improvement in flame speed. With 20% H2 and NSPD, flame speed reached 37 cm/s at flame temperature of 2040 K at ϕ = 0.8. Similar results were observed with 0% and 5% H2 and a flame temperature of 2200 K at ϕ = 1. Lowering the flame temperature reduced NOx emissions. Combining 20% H2 and NSPD also increased the flammability limit to ϕ = 0.35 at a flame temperature of 1350 K, allowing for self-sustained combustion even at low temperatures.

A rising number of aerospace manufacturers are working on the development of new solutions in the field of Urban Air Mobility with increasing attention addressing electric and hybrid electric propulsive systems. Hybrid electric propulsive systems potentially offer performance improvements during transient maneuvers, as well as sustaining the engine during flight phases characterized by high power demands. Among the challenges of hybridization in rotorcraft, there is the necessity to predict the dynamic behavior and its effect on the control of rotor shaft speed. In the present study, the dynamic behavior of a parallel hybrid electric propulsive system for a coaxial-rotor air taxi is analyzed in response to a typical sequence of pilot commands that encompasses the range of operations from hover to forward flight. The system is modeled with a dynamic approach and includes sub-models for the coaxial rotors, the turboshaft engine, the electric machine, and the battery. The results of the investigation show a better performance during transients of the hybrid system than a conventional turboshaft configuration, especially if the electric contribution to the power request is coordinated to account for the lag due to slower engine dynamic response.

Micro turbojets are used for propelling radio-controlled aircraft, aerial targets, and personal air vehicles. When compared to full-scale engines, they are characterized by relatively low efficiency and durability. In this context, the degraded performance of gas path components could lead to an unacceptable reduction in the overall engine performance. In this work, a data-driven model based on a conventional artificial neural network (ANN) and an extreme learning machine (ELM) was used for estimating the performance degradation of the micro turbojet. The training datasets containing the performance data of the engine with degraded components were generated using the validated GSP model and the Monte Carlo approach. In particular, compressor and turbine performance degradation were simulated for three different flight regimes. It was confirmed that component degradation had a similar impact in flight than at sea level. Finally, the datasets were used in the training and testing process of the ELM algorithm with four different input vectors. Two vectors had an extensive number of virtual sensors, and the other two were reduced to just fuel flow and exhaust gas temperature. Even with the small number of sensors, the high prediction accuracy of ELM was maintained for takeoff and cruise but was slightly worse for variable flight conditions.

The article presents the data regarding the experimental characterization of combustion of liquid jet A1 with addition of urea-water emulsion. A liquid-fuel gas turbine derived burner operating in non-premixed mode under three different equivalence fuel/air ratios was used. The data were collected, with and without urea addition, with two high speed visualization systems which acquired the broadband and spatially and spectrally resolved chemiluminescence emissions. Chemiluminescence images of OH* were acquired using an intensified camera system with a narrow-band filter at approximately 310 nm CWL, while the chemiluminescence images of CH* were recorded with a 436 nm CWL. Measurements of exhaust temperature and NOx, CO and CO2 emissions have been also performed. The data presented here are related to the article entitled “COMBUSTION PERFORMANCE OF A LOW NOx GAS TURBINE COMBUSTOR USING UREA ADDITION INTO LIQUID FUEL” [1].

Respiratory syncytial virus (RSV) infection is the most common cause of hospitalization in young children. In the last 2 years, public health measures aimed at controlling the spread of SARS-CoV-2 have affected the epidemiology and seasonality of RSV worldwide. The aim of this descriptive retrospective observational study was to describe the characteristics of children hospitalized with RSV in an academic tertiary care hospital in Southern Italy in 2021. We also investigate the seasonal trends of RSV from 2017 to 2021. The demographic characteristics, comorbidities, clinical data, and coinfections were retrospectively evaluated. Compared with previous seasons, the 2021 outbreak of RSV was characterized by an increased number of patients, with a delayed peak observed in November. Overall, 179 children, including 128 (71.5%) aged p < 0.05 each). History of prematurity was not significantly associated with the presence of coinfections. Because of the high burden of RSV infection and the expected larger RSV epidemics resulting from a greater number of RSV-naïve children, systematic epidemiological and virological surveillance is needed. Appropriate pathways for access to RSV prevention in all infants should also be introduced.

Horizontal gaze palsy with progressive scoliosis (HGPPS) is a rare congenital disease characterized by the absence of horizontal gaze movements, progressive scoliosis, and typical brain, cerebellum, and medullary malformations. Here we describe a pediatric HGPPS case with overlapping epilepsy and learning difficulties. A 6-year-old girl was admitted to the University Hospital of Bari for the onset of a tonic–clonic seizure. Electroencephalogram showed slow and sharp waves on the right side with the tendency to diffuse. Brain magnetic resonance imaging demonstrated malformations compatible with HGPPS. Ophthalmological and orthopedic evaluations confirmed conjugate horizontal gaze palsy and mild thoracolumbar scoliosis. Neuropsychological assessment attested normal intelligence but serious difficulties in reading and writing. In spite of neuroradiological malformations, visual difficulties, and spinal deformities, literature data are limited about any coexisting neurocognitive HGPPS symptoms. Literature data regarding such topics are very limited. If, on the one hand, the coexistence of such symptoms can be interpreted as occasional, it could support the idea that they could fall within a spectrum of HGPPS anomalies. In addition to the standard investigations, the activation of specific neuropsychological assessment programs could help interventions improve the specialist care and the quality of life of HGPPS patients.

An efficient maintenance plan is an important aspect for aeronautical companies to increase flight safety and decrease costs. Modern technologies are widely used in the Engine Health Monitoring (EHM) discipline to develop intelligent tools capable of monitoring the health status of engines. In this work, Artificial Neural Networks (ANNs) and in-detail Feed-Forward Neural Networks (FFNNs) were exploited in addition to a Kernel Principal Component Analysis (KPCA) to design an intelligent diagnostic tool capable of predicting the Performance Parameters (PPs) of the main components used as their health index. For this purpose, appropriate datasets containing information about degraded engines were generated using the Gas Turbine Simulation Program (GSP). Finally, the original datasets and the reduced datasets obtained after the application of KPCA to the original datasets were both used in the training and testing process of neural networks, and results were compared. The goal was to obtain a reliable intelligent tool useful for diagnostic purposes. The study showed that the degraded component detection and estimation of its performance achieved by using the hybrid KPCA–FFNNs were predicted with accurate and reliable performance, as demonstrated through detailed quantitative confusion matrix analysis.

Socio-ecologic, socio-economic, and socio-technical transitions are opportunities that require fundamental changes in the system. These will encounter matters associated with security, service adoption by end-users, infrastructure and availability. The purpose of this study is to examine and overcome the risks to take advantage of opportunities through the novel Risky-Opportunity Analysis Method (ROAM). A novel quantitative method is designed to determine when, after making some changes, the risks become acceptable so that the opportunity does not deviate from the objectives. The approach provided a quantitative evaluation of the possible changes in parallel with digitization, towards providing a green Service Supply Chain (SSC). The result of ROAM shows that the most cost-effective change to increase the resilience of the system is a solution (SMS) which is different from that identified by a TOPSIS multi-criteria method. Real-word decisions in change management should tackle the complexity of systems and uncertainty of events during and after transition through a careful analysis of the alternatives. A case-study was carried out to evaluate the alternatives of an ancillary service in the Payment Service Providers (PSP). The comparison of the ROAM results with the traditional TOPSIS of the case-study unveils the priority of the ROAM in practice when the alternatives are Risky-Opportunities. The existing risk assessment tools do not take advantage of risky opportunities. To this aim, the current article introduces the term Risky-Opportunity, and two indexes—Stress and Strain—of the alternatives that are designed to be employed in the new quantitative ROAM approach.

The present investigation addresses the topic of Urban Air Mobility with particular reference to the air-taxi service with electrified power systems. A new and detailed methodology is proposed for the simplified design and energy analysis of conventional, hybrid-electric, and full-electric power systems for this application. The original contributions to the scientific literature on UAM are the detailed modeling approach, the evaluation of CO2 emissions with a Well-to-Wing approach as a function of the electricity Emission Intensity factor, and the comparison with road vehicles performing the same route in different driving conditions. The comparison demonstrates the advantages of a full electric air-taxi with today’s technology versus a hybrid-electric road taxi, especially in cases involving low emission intensity and unfavorable driving conditions (congested traffic, aggressive driving style, and high circuity factor values). In the case of 2035 technology, the comparison with a referenced fully electric road vehicle is detrimental to the air taxi but the values of Well-to-Wheel/Wing CO2 with the expected Emission Intensity of 90 g/kWe for the European Union are still quite low (67 g/km). The investigation also quantifies the negative effect of battery aging on the consumption of the air taxi and on the number of consecutive flights that can be performed without fully charging the battery.