Energy consumption and environmental impact of Urban Air mobility

Urban Air Mobility (UAM) is a recent concept proposed for solving urban mobility problems, such as urban traffic pollution, congestion, and noises. The goal of this investigation is to develop a backward model for an electric aerial taxi in order to estimate the electric consumption and the indirect emissions of carbon dioxide in a specified mission. The model takes as input the time histories of speed and altitude and estimates the power at the rotor shaft during the mission with a quasi-static approach. The shaft power is used as input for the electric drive where the motor is modelled with an efficiency map and a transfer function while an equivalent circuit model which includes aging effects is used for the battery. The emissions of CO 2 are calculated as a function of the Greenhouse emission intensity and compared with that of a hybrid electric taxi performing the same mission with the same payload. A plug-in Toyota Prius modelled through the software ADVISOR is considered for the comparison. The results show that the air taxi behaves better than the road taxi not only in terms of trip time but also from the environmental point of view if the charging of the battery is performed with the emission intensity factory expected to be reached in Europe in 2025

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

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

The pyrolysis and gasification pathways of automotive shredder residue targeting the production of fuels and chemicals

Automotive shredder residue (ASR), also referred to as car fluff, is the 15-25% of end-of-life vehicle’s mass remaining after de-pollution, dismantling, shredding of the hulk and removal of metals from the shredded fraction. ASR typically consists of metals, plastics, rubber, textile, wood and glass, and is commonly landfilled. The use of ASR as a fuel in incineration processes is controversial since toxic pollutants can be generated as by-products if operational conditions and gas cleaning systems are not carefully controlled. Thermochemical treatment of ASR consists of advanced technology processes that convert ASR components liable to decomposition under the application of heat into liquids and/or gases and a solid residue containing metals. Within the thermochemical treatment options for ASR, pyrolysis and gasification are generally considered as the emerging technologies. The pyrolysis process uses medium temperatures (400-600°C) and an oxygen-free environment to decompose ASR chemically, thus producing minimum emissions and allowing metals to be recovered. Gasification is operated at higher temperatures (>700-800°C) and typically uses air as a gasification agent, which raises some issues in terms of emissions. Lab and pilot-scale plants fed with ASR have been built using both technologies, also considering a combination of them. The aim of this paper is the identification of the best conversion pathway for the production of transportation fuels, aviation fuels or chemicals (hydrogen, methanol, etc.) from ASR. The intermediate products from gasification and pyrolysis are used as feedstock in secondary processes for the production of the final products. The heterogeneous and complex composition of ASR raises several challenges upon its thermochemical treatment, so that the second step of the conversion process is typically not even addressed. Instead, this further step is fundamental to obtain some valuable products that can directly replace fossil derived fuels or chemicals. The updated picture presented in this work should help identify the main advantages and drawbacks of the pyrolysis and gasification processes when considered part of an overall ASR to fuels or chemicals plant

Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque

Combustion and performance characteristics of air-fuel mixtures ignited by means of photo-thermal ignition of Nano-Energetic Materials

Abstract This work presents an experimental investigation to determine the performance and characteristics of the combustion process triggered by a new ignition system based on photo-thermal effect, observed when nano-Energetic Materials are exposed to a flash light. The resulting combustion process has been compared with the one obtained using the spark-plug traditionally used in spark ignition engines. Results showed that the photo-thermal ignition determines higher combustion pressure gradient, peak pressure, total heat released, fuel combustion efficiency, and a shorter ignition delay and combustion duration compared with the spark ignition, for all the tested fuels and air-fuel ratios

A NEURAL NETWORK APPROACH TO ANALYSE CAVITATING FLOW REGIME IN AN INTERNAL ORIFICE

none3The identification of the water cavitation regime is an important issue in a wide range of machines, as hydraulic machines and internal combustion engine. In the present work several experiments on a water cavitating flow were conducted in order to investigate the influence of pressures and temperature on flow regime transition. In some cases, as the injection of hot fluid or the cryogenic cavitation, the thermal effects could be important. The cavitating flow pattern was analyzed by the images acquired by the high-speed camera and by the pressure signals. Four water cavitation regimes were individuated by the visualizations: no-cavitation, developing, super and jet cavitation. As by image analysis, also by the frequency analysis of the pressure signals, different flow behaviours were identified at the different operating conditions. A useful approach to predict and on-line monitoring the cavitating flow and to investigate the influence of the different parameters on the phenomenon is the application of Artificial Neural Network (ANN). In the present study a three-layer Elman neural network was designed, using as inputs the power spectral density distributions of dynamic differential pressure fluctuations, recorded downstream and upstream the restricted area of the orifice. Results show that the designed neural networks predict the cavitation patterns successfully comparing with the cavitation pattern by visual observation. The Artificial Neural Network underlines also the impact that each input has in the training process, so it is possible to identify the frequency ranges that more influence the different cavitation regimes and the impact of the temperature. A theoretical analysis has been also performed to justify the results of the experimental observations. In this approach the nonlinear dynamics of the bubbles growth have been used on an homogenous vapor - liquid mixture model, so to couple the effects of the internal dynamic bubble with the other flow parameters.Paper ESDA2012-82205M.G. De Giorgi; D. Bello; A.FicarellaDE GIORGI, Maria Grazia; Bello, Daniela; Ficarella, Antoni

optimization of plasma actuator excitation waveform and materials for separation control in turbomachinery

Abstract Different input waveforms applied to a Single Dielectric Barrier Discharge Plasma Actuator (SDBDPA) were compared for flow separation control on low-pressure turbines (LPTs). The investigated Reynolds number (Re) was 2·104. The work aim was the device optimization in terms of materials and excitation conditions for enhancing its durability and performances. The SDBDPA was manufactured by microfabrication techniques. Device materials that could withstand the plasma environment were selected. Sine, square and triangle waveforms were compared in terms of actuator dissipated power and induced velocity. At comparable peak-to-peak applied voltage, the sinus outperformed the other waveforms, while the square dissipated the most

cynara cardunculus and coffee grounds as promising biodiesel sources for internal combustion compression ignition engines

Abstract In this study, the effect of using two innovative biodiesels - derived respectively from coffee grounds and Cynara cardunculus - in blend with neat diesel fuel, on combustion and emissions in a compression ignition engine has been investigated. During tests, load and exhaust gas recirculation were varied and results compared with those obtained with neat diesel fuel and its blends with Brassica carinata or waste cooking oil derived biodiesels. Results show a reduction or a comparable NOx and CO emission levels using Cynara cardunculus and coffee ground compared to the other fuels tested, while PM and THC emissions are penalized. Fuel consumption, as expected, is slightly reduced. EGR reduces NOx levels, while CO, THC and PM are generally penalized

Optimal design of phononic media through genetic algorithm-informed pre-stress for the control of antiplane wave propagation

In this paper we employ genetic algorithms in order to theoretically design a range of phononic media that can act to prevent or ensure antiplane elastic wave propagation over a specific range of low frequencies, with each case corresponding to a specific pre-stress level. The medium described consists of an array of cylindrical annuli embedded inside an elastic matrix. The annuli are considered as capable of large strain and their constitutive response is described by the popular Mooney–Rivlin strain energy function. The simple nature of the medium described is an alternative approach to topology optimization in phononic media, which although useful, often gives rise to complex phase distributions inside a composite material, leading to more complicated manufacturing requirements