Environmental and energy sustainability concerns have catalyzed a global transition toward renewable biofuel alternatives. Among these, biodiesel stands out as a promising substitute for conventional diesel in compression-ignition engines, providing compatibility without requiring modifications to engine design. A comprehensive understanding of biodiesel’s physical properties is crucial for accurately modeling fuel spray, atomization, combustion, and emissions in diesel engines. This study focuses on predicting the physical properties of PODL20 and EB100, including liquid viscosity, density, vapor pressure, latent heat of vaporization, thermal conductivity, gas diffusion coefficients, and surface tension, all integrated into the CONVERGE CFD fuel library for improved combustion simulations. Subsequently, numerical simulations were conducted using the predicted properties of the biodiesels, validated by experimental in-cylinder pressure data. The prediction models demonstrated excellent alignment with the experimental results, confirming their accuracy in simulating spray dynamics, combustion processes, turbulence, ignition, and emissions. Notably, significant improvements in key combustion parameters, such as cylinder pressure and heat release rate, were recorded with the use of biodiesels. Specifically, the heat release rates for PODL20 and EB100 reached 165.74 J/CA and 140.08 J/CA, respectively, compared to 60.2 J/CA for conventional diesel fuel. Furthermore, when evaluating both soot and NO_x emissions, EB100 displayed a more balanced performance, achieving a significant reduction in soot emissions of 34.21% alongside a moderate increase in NO_x emissions of 45.5% compared to diesel fuel. In comparison to PODL20, reductions of 20.4% in soot emissions and 3% in NO_x emissions were also noted.

This study aims to develop an effective method for predicting the temperature and torque of Permanent Magnet Synchronous Motors (PMSMs) using deep learning techniques, which is crucial for optimizing motor performance and ensuring longevity, particularly in the automotive industry. Various Neural Network (NN) architectures, including a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (BiLSTM) unit, were employed to model the complex relationships between motor parameters, such as stator winding, current, torque, and permanent magnet temperature. The findings demonstrate that an NN with two hidden layers (64 and 32 neurons) achieved an R2 score of 0.99 for both torque and temperature prediction, while the BiLSTM network effectively modeled temporal dynamics, leading to high-fidelity rotor temperature predictions. This research provides a novel application of BiLSTM RNNs in accurately predicting PMSM temperatures, offering valuable insights for industries reliant on these motors. Integrating these models into motor control systems can enhance operational efficiency, reduce overheating risks, and extend motor lifespan, contributing to energy savings and environmental sustainability by lowering energy consumption and reducing waste.

Groundwater recharge is critical for sustainable water management in water-scarce regions like North Algeria, where climate change and urbanization exacerbate resource challenges, particularly in the populous Algiers watershed. This study evaluates groundwater recharge potential using the Analytical Hierarchy Process (AHP) and its fuzzy extension (FAHP), integrated with Geographic Information Systems (GIS), to map recharge zones. Employing open-source data, AHP and FAHP assessed factors such as lithology, slope, and rainfall, classifying the watershed into high, moderate, and low recharge potential zones. Results show AHP identifying 44.01% (528.95 km²) as high, 52.82% (634.93 km²) as moderate, and 3.18% (38.14 km²) as low potential, with FAHP yielding similar outcomes (44.35%, 52.47%, and 3.17%, respectively). Validation using borehole drawdown data confirmed a 73.3% accuracy and an AUC of 0.72, indicating moderate to good reliability. High recharge zones were concentrated in the central and northern areas with permeable soils and gentle slopes, moderate zones dominated the region, and low zones were minimal. This study concludes that both AHP and FAHP are effective for preliminary recharge assessments, with AHP favored for its simplicity, though FAHP excels with uncertain data. Limited high-resolution hydrogeological data highlight the need for further refinement, yet the approach offers a replicable framework for managing groundwater in arid, urbanized regions facing similar environmental pressures.