https://jrmam.sku.ac.ir/ پژوهش‌های مکانیک ماشینهای کشاورزی fa daily 1 Mon, 22 Jun 2026 00:00:00 +0330 Mon, 22 Jun 2026 00:00:00 +0330 https://jrmam.sku.ac.ir/article_116751.html This article critically analyses global renewable energy policies through 2025. It focuses on eight countries: Germany, Denmark, China, the United States, India, Brazil, the UAE, and Morocco. The analysis highlights cross-country differences in renewable energy use, costs, reliability, and public attitudes. While renewable capacity has grown and costs have fallen, significant challenges remain. High-penetration renewable systems are becoming less reliable. Policy changes in the United States threaten further progress. Tariff wars have delayed billions of dollars in clean energy projects. Land-use conflicts are also intensifying globally. By 2050, demand for critical minerals is expected to rise sharply. This will exacerbate child labor, water scarcity, and geopolitical risks in the Global South. Meanwhile, the early phase-out of nuclear power in Germany and Denmark has increased fossil fuel dependence and raised consumer prices. These outcomes reveal the limitations of relying solely on renewables. Small modular reactors (SMRs) are already in use in Russia and China. They are considered a promising low-carbon addition to renewable energy systems. Commercial deployment of SMRs is expected in Canada, the United Kingdom, and other countries between 2026 and 2030. This paper argues that a rapid, cost-effective, and fair transition to net zero requires specific policy measures. These include long-term revenue certainty, responsible mineral management, biodiversity protection, nuclear integration, and community benefit-sharing. Without these measures, the energy transition will be slower, more expensive, and less equitable. https://jrmam.sku.ac.ir/article_116752.html Thermal regulation of photovoltaic (PV) systems is critical for maintaining electrical efficiency and extending component lifespan. This study presents a hybrid photovoltaic/thermal (PV/T) collector incorporating nano-enhanced phase change material (nano-PCM) for simultaneous cooling and thermal energy storage. Copper tubes filled with paraffin doped with 3 wt.% zirconium oxide (ZrO₂) nanoparticles were vertically embedded beneath the PV panel in a novel energy storage configuration to enhance heat dissipation, improve thermal uniformity, and optimize latent heat storage within the collector structure. Transient numerical simulations were conducted using ANSYS Fluent to analyze both charging and discharging phases of the thermal storage system. During the charging process, the nano-PCM absorbed excess solar heat, effectively stabilizing the thermal field and maintaining tube temperatures approximately within the range of 290–293 K, while limiting the peak collector temperature to 372–389 K and preventing localized thermal hotspots. In the discharging phase, the controlled release of stored latent heat sustained tube temperatures between 310–325 K, maintained the central collector surface temperature at 340–345 K, and kept the PV panel temperature within 300–304 K, thereby ensuring stable operational thermal conditions. The proposed system introduces a modified and innovative configuration of thermal energy storage integration, enabling improved heat management, enhanced energy recovery, and prolonged system stability. The results indicate that the simultaneous implementation of nano-PCM–based storage and optimized PV/T structural arrangement provides an effective strategy for balancing heat absorption, storage, and controlled heat release. This integrated thermal management approach is particularly suitable for solar drying applications, where a continuous and stable thermal energy supply is essential for maintaining product quality and process efficiency. https://jrmam.sku.ac.ir/article_116753.html Given the importance of long-term preservation of pomegranate fruits and the reduction of postharvest losses, this study investigated the effects of chilling injury during storage. Chilling injury is one of the major problems affecting pomegranate quality during cold storage, often leading to changes in texture and internal chemical composition. This study aimed to investigate the effect of chilling injury on the physicochemical properties of 'Wonderful' pomegranate fruits at two experimental levels: fresh fruits (control) and fruits stored at 0±0.5°C for two months (chilling-injured). The measured parameters included firmness (kg/cm²), weight (g), soluble solids content (SSC, °Brix), pH, and titratable acidity (TA, % citric acid). Firmness was measured using a texture analyzer (kg/cm²), SSC with a digital refractometer (°Brix), pH with a pH meter, and TA by titration (% citric acid). The collected data were statistically analyzed using SAS to compare healthy and affected samples. This study provides the first quantitative comparison of physical and chemical properties in chilling-injured whole 'Wonderful' pomegranate fruits using multivariate analysis (PCA). Results showed that healthy fruits had significantly higher firmness than chilling-injured fruits (88.76 vs. 60.89 kg/cm², P < 0.05). Weight did not differ significantly between groups (143.38 g vs. 140.43 g, P > 0.05). SSC was higher in injured fruits (20.34 vs. 17.18 °Brix, P < 0.05). pH was lower in injured fruits (2.84 vs. 3.02, P < 0.05), while TA was higher (1.01% vs. 0.821% citric acid, P < 0.05). Overall, these findings indicate that Chilling injury leads to softer texture, increased SSC, and altered acidity. These changes suggest that chilling-damaged pomegranates are unsuitable for fresh consumption but may be redirected to juice processing, where higher SSC and TA could be advantageous. https://jrmam.sku.ac.ir/article_116754.html Due to the variable and non-deterministic nature of wind, accurate prediction of wind turbine power output is crucial for integrating this renewable energy source into the power grid and optimizing operational management. This study proposes a hybrid deep learning framework for accurate short-term prediction. The proposed model, by sequentially integrating a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network, can simultaneously extract and model local patterns and short-term dependencies through the CNN layers and long-term temporal dependencies through the LSTM layers from time-series data. The data used are real operational supervisory data from wind turbines in the Manjil region of Iran over a one-year period (from April 2024 to March 2025). After outlier removal, noise reduction, and normalization, a set of key variables including wind speed, rotor speed, generator speed, and temperatures of critical turbine components were selected as input features. The performance of the proposed hybrid CNN-LSTM model was compared with three baseline machine learning models (Linear Regression, Random Forest, and Gradient Boosting) based on the common evaluation metrics of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²). The results clearly demonstrate the superiority of the hybrid model. The CNN-LSTM model, with the lowest MAE and the highest coefficient of determination (R² = 0.892), performed significantly better than the comparative models. Correlation analyses revealed that wind speed-related variables had the greatest influence on power output. Furthermore, time-series and scatter plots demonstrated a close match between the model's predictions and actual values, effectively showcasing its ability to track complex fluctuations, seasonal variations, and even transient events. This study demonstrated that hybrid deep learning methods, due to their inherent ability to model non-linear and multi-scale relationships, represent an effective solution to the challenge of wind power forecasting. Applying this model to the operational data of a wind farm site constitutes a fundamental step towards localizing and developing intelligent management solutions for renewable energy in the country. https://jrmam.sku.ac.ir/article_116750.html This study aimed to develop and compare multivariate and stepwise regression models for predicting yield and energy indices in sugar beet (Beta vulgaris L.) cultivation. Field data were collected from 83 farms in Shahrekord County, Chaharmahal and Bakhtiari Province, Iran. Energy indices—including energy ratio (ER), energy productivity (EP), specific energy (SE), and net energy gain (NEG)—were calculated based on input and output energy equivalents. The results showed an average sugar beet yield of 56.11 t ha⁻¹, with corresponding ER of 40.89, EP of 2.43 kg MJ⁻¹, SE of 0.41 MJ kg⁻¹, and NEG of 919,750.28 MJ ha⁻¹. The multivariate regression model demonstrated superior predictive accuracy (full data R² = 98.8%, 5-fold Cross Validation R² ≈ 91.0%) compared to the stepwise model (full data R² = 95.7%, 5-fold Cross Validation R² ≈ 92.5%), owing to its inclusion of interaction and quadratic effects. Key interactions—including nitrogen × phosphate (positive), phosphate × micronutrients (negative), and human labor × nitrogen (positive)—highlighted the importance of balanced nutrient management. The highest energy input shares were attributed to diesel fuel and micronutrients (together accounting for >64% of total input energy), underscoring the need for improved machinery efficiency and targeted micronutrient application. For energy indices, multivariate regression achieved excellent fit (R² = 99.54% for ER and EP; R² = 97.66% for SE), with crop yield, micronutrients, diesel fuel, and nitrogen identified as the most influential variables. The optimal application ranges for maximizing yield were estimated at 80–90 kg ha⁻¹ (phosphate), 60–70 kg ha⁻¹ (micronutrients), and 60–70 kg ha⁻¹ (nitrogen). These findings provide practical insights for optimizing energy consumption, reducing environmental impacts, and enhancing sustainable sugar beet production in semi-arid agricultural systems. https://jrmam.sku.ac.ir/article_116755.html Accurate, computationally efficient classification of apple leaf diseases is essential to improving orchard productivity. Despite advances in deep learning, many existing models rely on intensive architectures that limit deployment in resource-constrained environments. This study proposes a lightweight CNN-MLP framework optimized for disease classification under heterogeneous visual conditions. Pre-trained ResNet-18 and EfficientNet-B0 backbones were utilized for feature extraction, with their original classification heads replaced by a task-specific, two-layer multilayer perceptron (MLP) head. This architectural adaptation, incorporating a 256-neuron hidden layer and dropout regularization, aims to enhance learning of non-linear boundaries in diverse datasets. An experimental evaluation on the 9-class AL9EE dataset, conducted across five independent runs, demonstrated that the EfficientNet-B0-MLP model achieved 98.42 ± 0.26% accuracy and a macro F1-score of 98.33 ± 0.34%, outperforming the ResNet-18-MLP variant. The lower standard deviations indicate improved training stability and reproducibility. By balancing predictive performance with reduced computational complexity, this framework provides a practical solution for edge-oriented agricultural applications, offering an effective pathway for intelligent disease monitoring in smart farming.