Search Results (32)

This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids. Full article

Background/Objectives: Liver cirrhosis is a critical chronic condition with increasing global mortality and morbidity rates, emphasizing the necessity for early and accurate diagnosis. This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. Methods: The methodology integrates stacked ensemble learning, multi-task learning (MTL), and transfer learning within an explainable artificial intelligence (XAI) context to improve diagnostic accuracy, reliability, and transparency. A hybrid model combining multiple pre-trained convolutional neural networks (VGG16, MobileNet, and DenseNet121) with XGBoost as a meta-classifier demonstrated robust performance in binary classification between healthy and cirrhotic cases. Results: The model achieved a mean accuracy of 96.92%, precision of 95.12%, recall of 98.93%, and F1-score of 96.98% across 10-fold cross-validation. For staging (mild, moderate, and severe), the MTL framework reached a main task accuracy of 96.71% and an average AUC of 99.81%, with a powerful performance in identifying severe cases. Grad-CAM visualizations reveal class-specific activation regions, enhancing the transparency and trust in the model’s decision-making. The proposed system was validated using the CirrMRI600+ dataset with a 10-fold cross-validation strategy, achieving high accuracy (AUC: 99.7%) and consistent results across folds. Conclusions: This research not only advances State-of-the-Art diagnostic methods but also addresses the black-box nature of deep learning in clinical applications. The framework offers potential as a decision-support system for radiologists, contributing to early detection, effective staging, personalized treatment planning, and better-informed treatment planning for liver cirrhosis. Full article

In this study, the carbon footprint of a steel-based windshield reinforcement component assembled in a sport utility vehicle was calculated in three different stages: steelmaking, stamping, and middle-of-use. Possible solutions to decrease carbon emissions were evidenced, such as the purchasing of steel made through low-impact technologies and the exploitation of the green energy grid to power up stamping machines. The life cycle assessment methodology was used to calculate the carbon footprint. Four different steels provided by different suppliers were compared in order to highlight the greenest material for both the steelmaking and stamping processes and the best supplier from an environmental point of view. In addition, the carbon footprint related to the component weight in vehicles with different traction set-ups, i.e., internal combustion engine, mild hybrid electric, and battery electric, was reported. To reduce the carbon footprint, electric arc furnace-based steelmaking and cold stamping were the best options. In addition, component weight reduction (for instance, changing materials) allowed a decrease in fuel and/or energy consumption, with carbon footprint benefits. Full article

High-efficiency and low-emissions heavy-duty (HD) internal combustion engines (ICEs) offer significant GHG reduction potential. Mild hybridization via regenerative braking and enabling the use of an electric heater component (EHC) for the aftertreatment system (ATS) warm-up extends these benefits, which can mitigate tailpipe GHG and NOx emissions simultaneously. Understanding such integrated hybrid powertrains is essential for the system optimization of real-world driving conditions. In the present work, the potential of a low engine-out NOx (1.5–2.5 g/kWh range) ‘Low-NOx’ HD diesel engine and EHCs were analyzed in a 48V P1 mild-hybrid system for a class 8 commercial vehicle concept and compared with those in an EPA-2010-certified HD diesel truck as a baseline under real-world driving cycles, including those from the US, Europe, India, China, as well as the world harmonized vehicle cycle (WHVC). For analysis, an integrated 1-D vehicle model was utilized that consisted of models of the ‘Low-NOx’ HD engine, the stock ATS, and a production EHC. For the real driving cycles, ‘GT-RealDrive’-based vehicle speed profiles were generated for busy trucking routes for different markets. For each cycle, the effects of the Low-NOx and EHC performances were quantified in terms of the ATS warm-up time, engine-out NOx emissions, and net fuel consumption. Depending on the driving route, the regenerative braking fully or partly neutralized the EHC power penalty without a significant impact on the ATS thermal performance. For a two-EHC system, the fueling penalty associated with every second reduction in the warm-up time ${FC}_{EHC}$ (g/s) was several-fold higher for the real driving routes compared with the WHVC. Overall, while a multi-EHC setup accelerated the ATS warm-up, a single EHC integrated at the SCR inlet showed minimized EHC heating power, leading to a minimized fueling penalty. Finally, for the India and China routes, being highly transient, the P1 hybridization proved inadequate for GHG reduction due to the limited energy recuperation. A stronger hybridization was desirable for such driving cycles. Full article

The pursuit of sustainable and environmentally friendly transportation has led to the exploration of alternative fuel sources, among which hydrogen stands out prominently. This work delves into the potential of hydrogen fuel for internal combustion engines (ICEs), emphasizing its capacity to ensure the required performance levels while concurrently enhancing overall efficiency. The integration of a mild hybrid powertrain in a small size passenger car was considered for obtaining a twofold advantage: mitigating power loss due to low volumetric efficiency and increasing fuel economy. A comprehensive approach combining 0D/1D modeling simulations and experimental validations was employed on a gasoline-powered small size ICE, considering its conversion to hydrogen, and mild hybridization. Vehicle simulations were performed in AVL Cruise M and validated against experimental data. Various electric motors were scrutinized for a small size battery pack typical of mild hybrid vehicles. Furthermore, the paper assesses the potential range achievable with the hydrogen-powered hybrid vehicle and compares it with the range reported by the manufacturer for the original gasoline and pure electric version. In terms of global results, these modifications were found to successfully improve efficiency compared to baseline gasoline and hydrogen fueling. Additionally, performance gains were achieved, surpassing the capabilities of the original gasoline vehicle despite its intrinsic volumetric efficiency limitations when using hydrogen. Along with the conversion to hydrogen and thus zero-carbon tail-pipe emissions, incorporating a Start/Stop system, and the integration of mild hybrid technology with energy recuperation during braking, overall efficiency was enhanced by up to 30% during urban use. Furthermore, the hybridization implemented in the H₂ version allows an autonomy comparable to that of the electric vehicle but with evident shorter refilling times. Specific aspects of the 48 V battery management are also scrutinized. Full article

Increasingly stringent pollutant emission regulations and a customer demand for a high-fuel economy drive the modern automotive industry to hurriedly solve the problem of decarbonization and powertrain efficiency, leading R&D towards alternative powertrain solutions and fuels. Electrification, today, plays the biggest role in the topic, with Mild Hybrid Electrified Vehicles (MHEVs) being the most cost-effective architectures, displaying dominance in smaller markets such as Brazil. One of the biggest challenges for HEVs’ development is the complexity of the hybrid control system, knowing when to actuate the electric machine, and the optimum power delivery, plus the gearshift schedule becomes a hard optimization problem that plays a key role in powertrain efficiency and cost savings for the customer. This paper proposes the implementation of a genetic algorithm (GA) as a machine learning-based control strategy to determine the torque split and the gear engaged for each driving condition of an MHEV operation, aiming to optimize fuel consumption. A quasi-static model of the vehicle was developed in Matlab/Simulink version 2022b, the virtual vehicle was then tested following the FTP75 and HWFET driving cycles. Simulation results indicate that the control decisions taken by the GA are qualitatively coherent for all operation conditions, and even quantitatively coherent in some cases, and that the software has the potential to be used as a control strategy outside the simulation environment, in future steps of development. Full article

Hybrid Electric Vehicles (HEVs) are a prominent solution for reducing CO₂ emissions from transport in Europe. They are equipped with at least two propulsion energy converters, an Internal Combustion Engine (ICE) and one or more Electric Machines (EMs), operated in a way to exploit synergies and achieve fuel efficiency. Because of the variety in configurations and strategies, the use of simulation is essential for vehicle development and characterisation of energy consumption. This paper introduces a novel simulation approach to estimate the CO₂ emissions from different hybrid architectures (series, parallel, power-split) and electrification degrees (mild, full, plug-in and range extender) that is relatively simple, flexible and accurate. The approach identifies the optimal power split between the energy converters for any given time in a driving cycle according to three evaluation levels: supervisor, ICE manager and optimiser. The latter relies on the Equivalent Consumption Minimisation Strategy (ECMS) and the limitations imposed by the other two layers. Six light-duty HEVs with different hybrid architectures were tested to support the development of the approach. The results show an indicative accuracy of ±5%, enabling to run assessments of hybrid powertrain solutions and supporting regulatory and consumer information initiatives. Full article

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers. Full article

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, load, gear shifts, battery state of charge (for hybrid vehicles), etc. In this paper, a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line-haul truck. The problem is solved using a combination of dynamic programming with backtracking and model predictive control. The specific fuel-saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features is a function of road grade. The result and analysis show significant improvement in fuel savings along with NOx benefits. Out of the control features, dynamic cruise (predictive) control and dynamic coasting showed the most benefits, while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency. Full article

An energy management control strategy based on the instantaneous optimization method of equivalent consumption minimization strategy (ECMS) under motor power compensation for mild hybrid vehicles is proposed in this study to improve fuel economy and ensure the dynamic performance of cars. A mild hybrid platform is built, and the future supplementary model of electric energy and the future consumption model of electric energy are established according to different power flow directions. It determines the equivalent fuel consumption rate of powertrain as the objective function by defining the equivalent factor and corresponding derivation, carries out optimization calculation, and obtains the energy distribution relationship between the engine and the motor. The motor power compensation strategy based on the control strategy is adopted to solve the effect of turbocharged engines’ transient response on vehicle dynamics and fuel economy. The actual results showed that vehicle power and fuel economy can be improved under the control strategy and compensation strategy design. Meanwhile, different motors allow the compensating coefficient to have different power-boosting and fuel economy effects. Full article

Calcium oxide (CaO) nanoparticles have recently gained much interest in recent research due to their remarkable catalytic activity in various chemical transformations. In this article, a chitosan calcium oxide nanocomposite was created by the solution casting method under microwave irradiation. The microwave power and heating time were adjusted to 400 watts for 3 min. As it suppresses particle aggregation, the chitosan (CS) biopolymer acted as a metal oxide stabilizer. In this study, we aimed to synthesize, characterize, and investigate the catalytic potency of chitosan–calcium oxide hybrid nanocomposites in several organic transformations. The produced CS–CaO nanocomposite was analyzed by applying different analytical techniques, including Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and field-emission scanning electron microscopy (FESEM). In addition, the calcium content of the nanocomposite film was measured using energy-dispersive X-ray spectroscopy (EDS). Fortunately, the CS–CaO nanocomposite (15 wt%) was demonstrated to be a good heterogeneous base promoter for high-yield thiazole production. Various reaction factors were studied to maximize the conditions of the catalytic technique. High reaction yields, fast reaction times, and mild reaction conditions are all advantages of the used protocol, as is the reusability of the catalyst; it was reused multiple times without a significant loss of potency. Full article

In order to meet the increasingly stricter emissions’ regulations, road vehicles require additional technologies aimed at the reduction of emissions from the internal combustion engine (ICE). A favorable solution from the standpoint of costs and simplicity of integration is a 48-V electrical architecture utilizing a low-voltage/high-power induction machine, which operates as the so-called engine belt starter generator (BSG) coupled via a timing belt with the ICE crankshaft within a P0 mild hybrid power train and used for starting up and boosting of the ICE power output, as well as for recuperating kinetic energy during vehicle deceleration. The aim of this work was to design a vibration damping system for the belt transmission within the so-called front end accessory drive (FEAD), which couples the BSG with the ICE crankshaft and to test the control system by means of simulations for realistic operating regimes of the P0 mild hybrid power train in order to show the functionality of the proposed approach in terms of mild hybrid vehicle performance improvement. Simulation results have pointed out effective attenuation of belt compliance-related vibrations using the proposed active damping control, with vibration magnitude reduced between three and five times compared to the default case during engine start-up phase. They have indicated the realistic belt slippage effects during engine start-up phase and have illustrated the effectiveness of the FEAD torque boosting capability with 30% gain in acceleration during vehicle launch. Full article

Research efforts into advanced emission control systems led to significant reduction of pollutant emissions of modern internal combustion engines. Sustainable renewable fuels are used to further reduce their Well-to-Wheels greenhouse gas emissions. The novel aspect of this paper is the compatibility investigation of existing advanced emission control technologies for achieving low pollutant emissions with the use of sustainable renewable fuels with vehicle tests. This is done on a diesel demonstrator vehicle, equipped with Lean NOx trap and dual-SCR technologies in combination with a 48V mild-hybrid powertrain. Tailpipe pollutant and CO₂ emissions are measured for market diesel fuel with 7% renewable fatty-acid-methyl-ester (FAME) (B7), diesel fuel with 30% FAME (B30), and 100% renewable hydrotreated vegetable oil (HVO). Results show no significant difference in pollutant emissions between the different fuels used. In a second part of the study, a Well-to-Wheels (WTW) analysis is conducted. This includes different pathways for the biomass-to-liquid fuels that were tested on the vehicle, as well as a power-to-diesel (e-diesel) assessment. Results show that significant WTW CO₂ reductions are possibly compared to the state-of-the-art market diesel fuel. Part of this reduction is already possible for the existing fleet as most of paraffinic compounds are drop-in for market diesel fuel. Full article

As part of a project for the realization of a hybrid vehicle with an innovative power train system, the proposal presented is to disconnect the turbocharger group and study the different behavior of the compressor and turbine, so decoupled. In an actual turbocharger, when the power of the turbine exceeds that required by the compressor, the wastegate valve opens. In this way, a part of the flue gases does not evolve into a turbine and limits the power generated. In the solution proposed here (the paper considers only “compressor side”) all the flow rate of the flue gases is processed by the turbine. In this way, for each rpms of the IC engine, the turbine generates more power than that required by the compressor. This makes it possible to use this surplus of power for the auxiliaries and/or to recharge the battery pack of the considered hybrid vehicle. An additional advantage is, thanks to this surplus generated, that the battery pack can be smaller and can be recharged while driving. Therefore, the entire system operates as a “Range Extended”. As mentioned above, this work is focused on the direct compressor—innovative electric motor coupling will be sized and realized, and a subsequent series of experimental tests will confirm the feasibility of this phase of the project. Full article

In Mediterranean environments, with mild winters and dry summers, peas are planted in autumn or early winter to profit from winter rain and to avoid terminal drought and high summer temperatures. The root parasitic weed broomrape (Orobanche crenata) appears as a major limiting factor under these conditions. To address such specific growing conditions and associated constraints, targeted breeding is needed. We present here recent achievements in the development of pea lines arising from a wide hybridization program incorporating resistance to broomrape and to powdery mildew (Erysiphe pisi) from landraces and wild relatives. Their adaption to autumn sowings under Mediterranean rain fed conditions, and their agronomic performance and resistance to prevailing diseases is compared with those of check cultivars in a multi-environment field test with nine trials performed over three seasons. HA-GGE biplots were a powerful tool for comparison among accessions in terms of performance and stability for each trait assessed. Like this, breeding lines NS22, NS34, NS8, NS39, NS35, NS21 and NS83 over-yielded all check cultivars. Grain yield was strongly affected by broomrape infection, with little influence of powdery mildew and ascochyta blight. All breeding lines studied showed high to moderate resistance to broomrape, whereas all check cultivars were severely infected. Broomrape infection was not correlated with days to flowering, whereas powdery mildew infection was favored by long cycles. Broomrape infection was enhanced by mild winter temperatures before flowering and spring rain, whereas high spring temperatures hampered broomrape development. Full article