Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 1: Radial Turbine Modelling and Design

The efficiency of Hybrid Electric Vehicles (HEVs) may be substantially increased if the energy of the exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered by means of a properly designed turbogenerator and employed for vehicle propulsion; previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% by employing a 20 kW turbine on a 100 HP rated power thermal unit. The innovative thermal unit here proposed is composed of a supercharged engine endowed with a properly designed turbogenerator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In these two parts, the realistic efficiency of the innovative thermal unit for hybrid vehicle is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors present a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, owing to some assumptions and approximations, allows a fast determination of the proper turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine considered is quite different from the ones commonly employed for turbocharging application; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In the Part 2 paper, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantage that could be achieved by the implementation of the turbogenerator on a hybrid propulsion system is evaluated through the performance prediction model under the different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbocompound system could gain vehicle efficiency improvement between 3.1% and 17.9%, depending on the output power level, while an average efficiency increment of 10.9% was determined for the whole operating range

Hybrid Propulsion Efficiency Increment through Exhaust Energy Recovery—Part 2: Numerical Simulation Results

The efficiency of hybrid electric vehicles may be substantially increased if the energy of exhaust gases, which do not complete the expansion inside the cylinder of the internal combustion engine, is efficiently recovered using a properly designed turbo-generator and employed for vehicle propulsion. Previous studies, carried out by the same authors of this work, showed a potential hybrid vehicle fuel efficiency increment up to 15% employing a 20 kW turbine on a 100 HP-rated power thermal unit. The innovative thermal unit proposed here is composed of a supercharged engine endowed with a properly designed turbo-generator, which comprises two fundamental elements: an exhaust gas turbine expressly designed and optimized for the application, and a suitable electric generator necessary to convert the recovered energy into electric energy, which can be stored in the on-board energy storage system of the vehicle. In this two-part work, the realistic efficiency of the innovative thermal unit for hybrid vehicles is evaluated and compared to a traditional turbocharged engine. In Part 1, the authors presented a model for the prediction of the efficiency of a dedicated radial turbine, based on a simple but effective mean-line approach; the same paper also reports a design algorithm, which, thanks to some assumptions and approximations, allows fast determination of the right turbine geometry for a given design operating condition. It is worth pointing out that, being optimized for quasi-steady power production, the exhaust gas turbine here considered is quite different from the ones commonly employed for turbocharging applications; for this reason, and in consideration of the required power size, such a turbine is not available on the market, nor has its development been previously carried out in the scientific literature. In this paper, Part 2, a radial turbine geometry is defined for the thermal unit previously calculated, employing the design algorithm described in Part 1; the realistic energetic advantages that could be achieved by the implementation of the turbo-generator on a hybrid propulsion system are evaluated through the performance prediction model under different operating conditions of the thermal unit. As an overall result, it was estimated that, compared to a reference traditional turbocharged engine, the turbo-compound system could gain vehicle efficiency improvement between 3.1% and 17.9%, according to the output power delivered, with an average efficiency increment of 10.9% evaluated on the whole operating range

Robust motion control of nonlinear quadrotor model with wind disturbance observer

This paper focuses on robust wind disturbance rejection for nonlinear quadrotor models. By leveraging on nonlinear unknown observer theory, it proposes a nonlinear dynamic filter that, using sensors already on-board the aircraft, can estimate in real-time wind gust signals in the three dimensions. The wind disturbance is then treated as input to the PD controller for a quick and robust flight pathway in presence of disturbances. With this scheme, the wind disturbance can be precisely estimated online and compensated in real-time. Hence, the quadrotor can successfully reach its desired attitude and position. To show the effective and desired performance of the method, simulation results are presented in Matlab/Simulink and ROS-enabled Gazebo platform

A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino

Chitinase and insect meal in aquaculture nutrition: a comprehensive overview of the latest achievements.

The aquaculture industry is looking for sustainable alternatives to conventional fish meals in fish feed, and insect-based meals are proving to be a promising solution. These meals are nutritionally optimal as they have a high protein content and an ideal amino acid profile. However, the presence of chitin, a component of the insect exoskeleton in these meals presents both an opportunity and a challenge. Chitosan, a derivative of chitin, is known to improve the physiological functions of fish, including growth, immunity, and disease resistance. While chitin and its derivative chitosan offer several physiological benefits, their presence can affect the digestibility of feed in some fish species, making the inclusion of insect-based meals in aquafeeds complex. While studies suggest positive effects, some problems, such as reduced growth rates in certain species, emphasize the need for further research on chitin digestion in fish. Chitinase, an enzyme that breaks down chitin, is being investigated as a potential solution to improve the nutritional value of insect meals in aquafeed. This review provides a comprehensive analysis of the applications, benefits, and challenges of using chitinase in aquaculture, highlighting the enzyme’s role in improving feed digestibility, disease control, and environmental sustainability. Extensive research is required to fully understand the potential of chitinase enzymes in aquaculture and to optimize their applications in this dynamic field. Overall, this review provides insight into the evolving landscape of insect-based meals and the applications of chitinase enzymes within sustainable aquaculture practices

Inhibitors of antibiotic resistance mechanisms: clinical applications and future perspectives

Bacterial strains responsible for antibiotic resistant infections are increasing in an alarming way and the evolution of resistance mechanisms seems to be unstoppable. In the past decade, many efforts have been made in order to counteract this phenomenon but very few compounds have reached clinical trials. The development of new classes of antibiotics able to overcome the main bacterial drug resistance mechanisms is urgently required to counter the imminent danger of a postantibiotic era

Intrathoracic impedance and pulmonary wedge pressure for the detection of heart failure deterioration

n/

A Win-Win Scheme for Improving the Environmental Sustainability of University Commuters’ Mobility and Getting Environmental Credits

European Union Member States are called upon to meet internationally proposed environmental goals. This study is based, in particular, on the recommendation of the European Union (EU), which encourages Member States to pursue effective policies to reduce greenhouse gas (GHGs) emissions, including through appropriate changes in the behavioral habits of citizens. In this respect, among the main sectors involved, transport and mobility should certainly be mentioned. National institutions should be adequately involved in order to achieve the objectives set; in this regard, universities must certainly be considered for their educational value. These latter, for instance, could commit to improving the environmental performance of the mobility of their commuter students (to a not insignificant extent), since commuting modes are often the cause of high CO2 emissions; indeed, they still largely involve the use of internal combustion engines based on fossil fuels. In this paper, the effectiveness of a smartphone-app-based method to encourage commuter students to adopt more sustainable transport modes is evaluated. In more detail, starting from a statistical analysis of the status quo of mobility habits of a sample of students at the University of Palermo (Italy), an improvement of current habits toward a more sustainable path is encouraged through a new application (specifically created for this purpose) installed on students’ smartphones. Then, the daily and annual distances traveled by commuters with the new mobility modes are calculated, and the resulting savings in energy and CO2 emissions are estimated. Finally, it is proposed that the reduced emissions could be converted into energy-efficiency credits that the University could use to enter the emission trading system (ETS), here contextualized within the Italian “TEE” (“Energy Efficiency Credits”) scheme, while the benefits for students participating in the program could consist of reduced fees and free access to university services. The results obtained show the feasibility of the proposal. This approach can be considered a useful model that could be adopted by any other public institutions—not only universities—to facilitate their path toward decarbonization