Abstract: This study investigates the influence of array height, irradiance, and wind speed on temperature difference and thermal gradients in photovoltaic (PV) arrays operating in hot, arid conditions. A field experiment was conducted in Mesa, Arizona (latitude 33° N), using two fixed-tilt PV module arrays installed at different elevations—one at 1 m and the other at 2 m above ground level. Each array comprised seven monocrystalline PV modules arranged in a single row with an 18° tilt angle optimized for summer performance. Data were collected between June and September 2025 and the analysis was restricted to 10:00–13:00 h to avoid shading and ensure uniform irradiance exposure on both arrays. Measurements included module backsheet temperatures at the center and edge modules, ambient temperature, plane-of-array (POA) irradiance, and wind speed. By maintaining identical orientation, tilt, and exposure conditions, the evaluation isolated the effect of height on module operating temperature and intra-array thermal gradients. Results indicate that the 2 m array consistently operated 1–3°C cooler than the 1 m array, confirming the positive impact of elevation on convective cooling. This reduction corresponds to a 0.4–0.9 % improvement in module efficiency or power based on standard temperature coefficients of crystalline silicon modules. The 1 m array exhibited a mean edge–center temperature gradience of −1.54°C, while the 2 m array showed −2.47°C, indicating stronger edge cooling in the elevated configuration. The 1 m array displayed a broader temperature range (−7 °C to +3°C) compared to the 2 m array (−5°C to +2°C), reflecting greater variability and weaker convective uniformity near ground level. The temperature gradience became more negative as irradiance increased, signifying intensified edge cooling under higher solar loading. Conversely, wind speed inversely affected ΔT, mitigating thermal gradients at higher airflow velocities. Overall, elevating PV arrays enhances convective heat transfer, reduces module temperature, and improves reliability and power output. These findings highlight the importance of array height, array length, irradiance, and wind conditions in optimizing PV system thermal and electrical performance.

Abstract:

Reduce fuel costs, improve waste utilization, and enhance energy efficiency by steaming mushroom substrate cubes using a mixed-fuel burner and furnace system that uses crude glycerol and used vegetable oil as alternative low-cost energy sources. This was the objective of this study. The experimental method measured boiler performance, exhaust-gas composition, temperature profiles, steam generation, and combustion-gas distribution inside the furnace. It was supported by analytical modeling of pressure, temperature, and combustion-gas distribution. Five fuel mixtures were prepared and tested, including 100% used vegetable oil, 100% glycerol, and 50/50, 25/75, and 10/90 blends. The tests were conducted in accordance with DIN EN 203-1. Blending used vegetable oil with glycerol improves flame stability, increases peak temperatures, and reduces the formation of incomplete combustion products compared to pure glycerol. The results also show that the mixture achieves high combustion efficiency (≈90-99%) and boiler thermal efficiency (≈72-73%). The optimal blend for stability, efficiency, and cost savings was 25/75 glycerol and vegetable oil. By cutting yearly fuel expenses by almost half, reducing steaming time by 2 hours per batch, and achieving a quicker payback period (3.26 months), the mixed-fuel system proved to be economically more advantageous than LPG, making it evidently practicable for agricultural producers. This study's conclusions suggest that, to maximize the use of renewable waste fuels and improve long-term sustainability, the following actions should be taken: further optimizing the air-fuel mixing process to improve combustion of higher-glycerol blends; scaling the system for larger mushroom farms; and expanding testing to other agricultural heating applications.

Abstract:

Oil spills pose severe ecological and economic threats, making rapid detection and severity assessment essential for effective environmental response and mitigation. Traditional remote-sensing approaches rely heavily on manual interpretation or rule-based algorithms, both of which are limited by variability in weather, illumination, and sea conditions. With the growing availability of satellite imagery and advancements in artificial intelligence, deep learning techniques offer powerful alternatives for automated oil spill identification. This study develops and evaluates a two-stage deep learning pipeline designed to (1) detect and segment oil spill regions in satellite images using semantic segmentation, and (2) classify the severity of identified spills using a supervised image-level classifier. The project utilizes the publicly available altunian/oil_spills dataset, consisting of 1,040 paired satellite images and color-encoded segmentation masks representing four classes: Background, Water, Oil, and Others. Stage 1 of the pipeline employs a U-Net architecture with a ResNet-18 encoder pretrained on ImageNet. The model performs pixel-level segmentation to isolate oil regions from surrounding ocean and environmental structures. Stage 2 uses a modified ResNet-18 classifier that accepts four-channel one-hot encoded segmentation outputs and predicts one of three spill severity levels derived from the proportional area of oil pixels: No Oil (<5%), Minor (5–15%), and Major (>15%). The pipeline was trained using the PyTorch framework with separate training cycles for each stage, enabling modular evaluation and interpretability. A systematic experimental setup including an 80/10/10 training–validation–test split, cross-entropy loss functions, Adam optimization, and 20-epoch training windows was used to assess model performance. Results show that the U-Net segmentation model achieves a mean Intersection-over-Union (IoU) of 0.8156 on the test set, with particularly strong performance on the Background (0.9123) and Water (0.8567) classes and lower, but still effective, performance on the Oil class (0.7234). These findings reflect the inherent class imbalance in satellite imagery, where oil occupies a small proportion of total pixels. The ResNet classifier achieved an overall accuracy of 88.76%, with F1-scores of 0.90 for No Oil, 0.85 for Minor, and 0.90 for Major severity levels. Classification errors were concentrated around the Minor category, consistent with threshold-based class definitions and segmentation uncertainty. The combined results demonstrate that a two-stage deep learning approach offers substantial improvements in both accuracy and interpretability over single-stage or heuristic-based systems. Segmentation masks provide visual justification for classification outputs, enabling a more transparent workflow for environmental monitoring agencies. Despite strong performance, limitations include dataset size, imbalance across severity classes, and dependency of classification accuracy on segmentation quality. Future work may incorporate data augmentation, advanced architectures such as U-Net++ or DeepLabv3+, temporal satellite imagery, or uncertainty quantification models for risk-aware operational deployment. Overall, our two-stage pipeline provides a robust, interpretable, and scalable framework for real-time oil spill detection and severity assessment in satellite imagery.

Abstract: Ensuring the structural integrity of high-energy piping systems is a critical requirement for the safe operation of nuclear power plants. This paper presents the design, imple-mentation, and three-year operational validation of a novel three-dimensional dis-placement monitoring system installed on the Steam Generator Blowdown pipeline of the Krško Nuclear Power Plant. The system was developed to confirm plant operating procedures will not cause excess dynamic displacements during operation. The measurement system configuration utilizes three non-collinear inductive dis-placement transducers (HBM WA/500 mm-L), mounted via miniature universal joints to a reference plate and to a defined observation point on the pipeline. The arrangement enables real-time monitoring of X, Y, and Z displacements within a spherical meas-urement volume of approximately 0.5 m. Data are continuously acquired by an HBM QuantumX MX840B amplifier and processed using CATMAN Easy-AP software through a fiber-optic communication link between the containment and control areas. The system has operated continuously for more than three years under elevated tem-perature and radiation conditions, confirming its reliability and robustness. The corre-lation of measured displacements with process parameters such as flow rate, pressure, and temperature provides valuable insight into transient events and contributes to predictive maintenance strategies. The presented methodology demonstrates a practical and radiation-tolerant approach for continuous structural monitoring of nuclear plant piping systems.

Abstract:

This work investigates the performance of a solar air heater (SAH) equipped with ten baffles whose angles can be adjusted in real time by a PLC. Many SAH systems operate passively, which makes their outlet temperature sensitive to daily variations in solar radiation. This study aims to show that an actively controlled SAH can maintain stable and efficient operation under practical outdoor conditions. Experiments were carried out at two set-point temperatures commonly used in drying applications, 54 °C and 60 °C, and the system was assessed through energy, exergy, and sustainability indicators. Greater baffle inclination increased turbulence and heat transfer, yielding thermal efficiencies up to 76.8%. The friction factor followed the Reynolds number closely, indicating that overall flow resistance depends mainly on the airflow rate. Exergy efficiency remained between 1.24% and 2.69%, while the Sustainability Index stayed near unity due to fan power related losses. A regression model was also developed to estimate the airflow needed to keep the outlet temperature at the desired level. Long-term projections show that the system can supply 20–22 MWh of heat and avoid nearly 9 tons of CO₂ emissions over 20 years. These findings highlight that combining PLC-based control with adjustable baffles offers a practical and environmentally meaningful improvement for solar air heating systems.

Abstract: Ammonia, as a carbon-free energy carrier, is gaining prominence for hydrogen storage and power generation applications due to its high energy density and ease of transport. However, the practical adoption of ammonia in combustion systems faces major stability challenges—chiefly its low reactivity, slow laminar burning velocity, narrow flammability envelope, and high ignition temperature. These attributes increase the risks of flame instability, misfire, and incomplete combustion, which, in turn, can elevate levels of unburned ammonia and greenhouse gas emissions such as NOx—posing significant health and climate concerns. Stable ammonia combustion demands optimization of several interrelated factors: the air–fuel equivalence ratio, flame temperature, flow regime, and combustor design are critical for maintaining reliable operation. Particularly pivotal is the control of the air–fuel equivalence ratio; excessively lean conditions can trigger flameout. Modern systems utilize real-time monitoring of flame and exhaust properties to diagnose and prevent instabilities. Advanced combustion strategies, such as transitioning to diffusion or flameless (MILD) regimes, substantially expand the stable operating window, especially under lean conditions. Overall, sustaining stable ammonia combustion is essential for maximizing efficiency and emission control, and integrating aftertreatment (deNOx) technologies is crucial for sustainable, clean-energy implementation.

Abstract: This study presents a techno-economic assessment of a modular solar-assisted me-thane pyrolysis pilot plant designed for sustainable hydrogen production in Nigeria using Concentrated Solar Power (CSP). Driven by the need to convert flare gas into value and reduce emissions, the work evaluates a hypothetical 100 kg/day hydrogen system by integrating a methane pyrolysis reactor with a solar heliostat–receiver field. Process modelling was carried out in DWSIM, while solar concentration behavior was represented using Tonatiuh. Mass and energy balance results show a hydrogen output of 3.95 kg/h accompanied by 12.30 kg/h of carbon black, with the reactor demanding roughly 44 kW of high-temperature heat at 900 °C. The total capital cost of the ≈50 kW pilot plant is approximately $1.5 million, with heliostat and receiver technologies forming the bulk of the investment. Annual operating costs are estimated at $69,580, along-side feedstock expenses of $43,566. Using annualized cost and discounted cash flow approaches, the resulting levelized cost of hydrogen (LCOH) is $5.87/kg, competitive with off-grid electrolysis in the region, though still above blue and gray hydrogen benchmarks. Financial indicators reveal a positive NPV, a 13% IRR, and a 13-year dis-counted payback period, highlighting the promise of solar-assisted methane pyrolysis as a transitional hydrogen pathway for Nigeria.

Abstract: This study introduces an integrated Life Cycle Assessment–Multi-Criteria Decision Analysis–Nash Equilibrium (LCA–MCDA–NE) framework to assess the feasibility of hydrogen energy storage (HES) in Philippine island grids. It starts with a cradle-to-gate LCA of hydrogen production across various electricity mix scenarios, from die-sel-dominated Small Power Utilities Group (SPUG) systems to high-renewable config-urations, quantifying greenhouse gas emissions. These impacts are normalized and in-tegrated into an MCDA framework that considers four stakeholder perspectives: Regu-latory (PRF), Developer (DF), Scientific (SF), and Local Social (LSF). Attribute utilities for Maintainability, Energy Efficiency, Geographic–Climatic Suitability, and Regulatory Compliance inform a 2×2 strategic game where net utility gain (Δ) and switching costs (C₁, C₂) influence adoption behavior. The findings indicate that the baseline Nash Equilibrium favors non-adoption due to limited utility gains and high switching barriers. However, enhancements in Main-tainability and reduced costs can shift this equilibrium toward adoption. The LCA results show meaningful decarbonization occurs only when low-carbon generation exceeds 60% of the electricity mix. This integrated framework highlights that successful HES de-ployment in remote grids relies on stakeholder coordination, reduced risks, and access to low-carbon electricity, offering a replicable model for emerging economies.

Abstract: To investigate the influence of different tip clearances on the hydraulic performance and cavitation characteristics of a cryogenic inducer, this study builds upon previous research by employing the SST k-ω turbulence model and modifying the empirical coefficients for evaporation and condensation in the Zwart cavitation model. Numerical simulations of the full flow field within an LNG cryogenic inducer were conducted. The results yielded cavitation performance curves, pressure distributions at incipient cavitation, vapor volume fraction contours, and leakage flow streamlines for various tip clearances. The impact of tip clearance on the overall hydraulic performance and cavitation behavior of the LNG inducer was systematically examined, with particular attention given to the microscopic evolution of the Tip Leakage Vortex (TLV) during the initial stages of cavitation. Experimental findings indicate that as the tip clearance increases, the tip leakage flow intensifies, leading to greater energy losses within the inducer and a consequent slight reduction in pump head and efficiency. A critical clearance value, δ, exists within the range of 0.4 mm to 0.6 mm, which governs the development pattern of the TLV. When the clearance is smaller than δ, the TLV forms more rapidly, and cavitation development is significantly more sensitive to increases in tip clearance. Conversely, when the clearance exceeds δ, the formation of the TLV is delayed, and cavitation progression becomes less responsive to further increases in tip clearance.

Abstract: This paper investigates the comprehensive energy profile and renewable energy solutions for a rural village comprising 30 houses. The study begins by analyzing the load demand distribution across day and night periods, with maximum daytime consumption recorded at 57.860 kWh and total daily usage of 509.040 kWh, while nighttime consumption peaks at 11.060 kWh with a total of 80.460 kWh. Detailed hourly consumption patterns are presented for various village components, including residential, water pumping, street lighting, medical facilities, and a supermarket, offering a granular view of energy use. To address the village’s energy requirements sustainably, a photovoltaic (PV) system with a capacity of 60 kW is proposed, supplemented by a solar thermal water heating system designed to meet hot water demands efficiently. The paper outlines the design and simulation of the solar water heating system, including calculations for water tube diameters, thermal resistance, and necessary tube length to transfer absorbed solar energy. MATLAB (V.22b) simulations further illustrate the performance of the integrated system, modeling energy production, battery charging/discharging cycles, and temperature fluctuations in water systems over a 24-hour period. Comparative analyses between standalone PV, PV/T hybrid, and combined PV plus solar thermal solutions reveal that the most cost-effective and maintenance-efficient strategy involves separate PV and thermal installations. The study highlights significant cost savings and environmental benefits over traditional diesel-based systems, positioning solar technologies as a reliable, sustainable, and economically viable solution for rural electrification and domestic hot water supply. Additional technical analysis confirms system sustainability and economic efficiency in real practice.

Abstract: This work examines the behaviour of a spark-ignition engine using oxy-fuel combustion, coupled with an oxygen production cycle based on a mixed ionic-electronic ceramic membrane. Through 1D-0D simulations, two compression ratios are studied: the original ratio of 9.6 and the optimised CR of 20, under various load levels and altitude conditions. The results show that operational limits exist at part-load conditions, where reducing the load without implementing additional control strategies may compromise system performance. It is observed that at low loads, the intake pressure can fall below atmospheric pressure, encouraging the presence of N2 in the combustion process. Additionally, the engine can operate efficiently up to an altitude of 4,000 m, although increasing boosting is required to maintain proper membrane conditions. These findings emphasise the importance of load control and the potential need for energy assistance under certain circumstances.

Abstract: A computational fluid dynamics analysis of the anode side of a proton exchange membrane (PEM) electrolyzer cell has been conducted. The geometry is symmetrical and allows for the investigation of a single feed channel and a single exhaust channel in an interdigitated flow field. The model utilizes the Eulerian approach and thus solves a full set of conservation equations for both gas and liquid phase. Moreover, it is non-isothermal and it includes phase change of water. The operating stoichiometric flow ratio results in segregated flow in the horizontal flow channels. At a current density of 1.0 A/cm², a local hot spot with a temperature increase of 7 °C is predicted. A reduction in the operating pressure below atmospheric pressure results in a more favorable concentration ratio of water vapor to oxygen at the PTL/CL interface, and the temperature distribution is more even. However, when the outlet pressure is too low, the outlet temperature is below the inlet temperature which makes this operation mode unfeasible. Adjustment of the back pressure can generally be used to control the temperature of the electrolyzer.

Abstract: Accurate binarization of phase-detection probe signals (gas vs. liquid) is necessary for the estimation of local void fraction, interfacial velocity, and bubble statistics in gas–liquid flows. Classical threshold based methods—single or double level— performs well on clean laboratory signals but degrades under realistic industrial conditions where noise, baseline drift, and clustered (slug-like) events challenge fixed rules. This work investigates whether deep learning (DL) models trained exclusively on synthetic data can deliver robust, generalizable binarization on real probe measurements. We (i) build a parametric generator of realistic time series from bubbly pulse templates, extended to clusters/slug patterns and perturbed with controlled noise, drift, and oscillatory baselines; (ii) train four lightweight DL architectures—one-dimensional U-Net (UNET-1D), Temporal Convolutional Network (TCN), a minimal one-dimensional Convolutional Neural Network (CNN-1D), and a Bidirectional Long-Short Memory network (BiLSTM)—only on synthetic signals; and (iii) evaluate them against classical threshold methods using event-level and sample-level metrics. On synthetic signal evaluation, UNET-1D and TCN achieve near-perfect event detection and sub-millisecond onset errors. On real bubbly and slug flow sensor data, classical threshold based methods remain highly competitive on clean sensor signals, while DL models retain advantages under non-stationary baselines and clustered events, yielding accurate void and timing with no hand-tuned assumptions. Results support DL as a practical, data-driven complement to fixed algorithms, particularly in noisy or drift-dominated measuring conditions.

Abstract: In this study, we propose a dynamic fractal dimension modeling (DFDM) framework that integrates image analysis, wavelet-based fractal methods, and structural fractal geometry to quantify the evolution of pore complexity. Unlike conventional static fractal approaches, our method captures time-dependent scaling laws and captures the spatiotemporal evolution of pore networks. The results demonstrate that dynamic fractal dimensions provide a robust descriptor of multi-scale heterogeneity, effectively bridging pore-scale processes with reservoir-scale behavior. This framework not only advances the theoretical understanding of fractal pore dynamics but also establishes a predictive tool with potential applications in unconventional hydrocarbon recovery, geological CO₂ sequestration, and multi-phase flow in porous media.

Abstract: Accurate estimation of the open-circuit voltage (OCV) as a function of state of charge (SOC) is fundamental for reliable battery management system (BMS) design in lithium-ion battery applications. However, at low temperatures, traditional low-rate OCV testing methods suffer from polarization-induced voltage drops that truncate the measured voltage range. This results in capacity underestimation and distorted OCV–SOC profiles, directly impacting SOC estimation accuracy. In this paper, we demonstrate how low-temperature conditions can severely distort the OCV-SOC models due to elevated polarization leading to premature voltage cutoffs. This paper presents a novel offsetting based correction method that extrapolates the charge/discharge curves beyond polarization-induced cutoff points to recover the full OCV span that otherwise would be lost at low temperatures. The approach is demonstrated using experimental low-rate OCV characterization data collected from Samsung EB575152 Li-ion cells from negative −25°C to 50°C. Results show that the proposed method significantly restores the usable OCV-SOC profile without requiring any modifications to the standard low-rate test protocol. By preserving complete voltage curves across a wide temperature range, this technique significantly improves the SOC estimation accuracy for battery management system.

Abstract: The prediction of energy consumption in battery electric vehicles (BEV) is a complex task due to the large number of influencing factors and their intercorrelation. However, it is a necessary endeavour to reduce range anxiety, facilitate route planning, manage charging infrastructure, and enable more effective travel decisions that lower operational risks in transportation; this would lead to greater adoption of BEV in the global vehicle fleet. In this regard, the present paper examines the available evidence on the methodologies employed for predicting the energy consumption of electric cars using the systematic literature review (SLR) protocol of Denyer and Tranfield together with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement for the selection and evaluation of studies. The analysis addresses modelling methods, computational tools employed, model accuracy metrics, the topology of the variables used, their sampling frequency and period of analysis, the modelling scale, and the data source. In addition, a classification of the different methodologies and variables is proposed, providing a reference framework for further studies. This article closes the research gap and complements previous literature, allowing the identification of current shortcomings and directions for future research related to energy consumption in BEV.

Abstract: Helically coiled tube heat exchangers (HCTHEX) have been the focus point in many experimental and numerical studies due to their compactness and high convective heat transfer coefficients on both the tube and shell sides. Previous studies have somehow established shell-side irregular behavior versus the geometrical and fluid dynamics of the coil side. At the same time, no definitive conclusion has been drawn regarding a more narrow range of characteristics of such heat exchangers (HEX). The current, more streamlined numerical analysis will shed light on a better understanding of the characteristics of such heat exchangers, following up on the previous studies by the corresponding author on the shell-side convective behavior. Deduced from an exhaustive range of simulations, a global correlation for the Nusselt number is proposed.

Abstract: The rapid evolution of energy systems and the increasing penetration of renewable energy sources have transformed traditional power grids into complex, dynamic smart grids (SG). Effective management of these systems requires integrated strategies that address technical, operational, and cyber-physical challenges. This paper presents a comprehensive framework for smart grid management that spans assessment, vulnerability analysis, blackout prevention, optimization, and resilience enhancement. The approach begins with multi-criteria assessment techniques to evaluate grid performance across reliability, efficiency, and sustainability dimensions. Advanced control algorithms and predictive analytics are then applied to optimize load balancing, energy storage, and distributed generation coordination. Finally, resilience strategies—encompassing cybersecurity measures, fault-tolerant architectures, and adaptive recovery mechanisms—are integrated to ensure stability under adverse conditions. The proposed framework underscores the importance of interoperability, data-driven decision-making, and cross-domain collaboration in achieving robust and sustainable smart grid operations.

Abstract: This paper reviews three mainstream technical routes for producing hydrogen from offshore wind power: offshore distributed hydrogen production, offshore centralized hydrogen production, and onshore hydrogen production. Drawing on global engineering cases, we analyze the characteristics, application scenarios, and current development status of each route, with particular attention to economic performance, system efficiency, and environmental adaptability. The main challenges identified include the limited adaptability of electrolysis technologies, high full-life-cycle costs, and persistent bottlenecks in storage and transportation technologies. Building on these findings, we summarize technological development trends and propose future directions in areas such as electrolyzer innovation, system efficiency optimization, direct seawater utilization, and storage and transport infrastructure. This review aims to provide a reference for advancing research, development, and large-scale application of offshore wind-to-hydrogen technologies.

Abstract: This study presents a one-dimensional numerical simulation of particulate matter (PM) oxidation and regeneration behavior in gasoline particulate filters (GPFs) under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions. The model incorporates both catalyst activity—represented by activation energy (E) and pre-exponential factor (A)—and exhaust control strategies involving forced fuel cut (FC). PM deposition and oxidation were simulated based on solid-state and gas-phase reactions, with the effects of oxygen concentration and temperature analyzed in detail. The results show that under high catalyst activity (E = 100 kJ mol⁻¹, A = 6.2 × 10⁷), PM oxidation proceeds efficiently even during medium-speed phases, achieving a 98.8% oxidation rate after one WLTC cycle. Conversely, conventional catalysts (E = 120 kJ mol⁻¹, A = 6.2 × 10⁶) exhibited limited regeneration, leaving 0.11 g of residual PM. Introducing forced FC effectively enhanced oxidation by increasing oxygen concentration to 20% and sustaining heat release. A single continuous 100-s FC yielded the highest oxidation (96% reduction), while split FCs reduced peak PM accumulation. These findings demonstrate that optimizing the balance between catalyst activity and FC control can significantly improve GPF regeneration performance, providing a practical strategy for PM reduction in GDI vehicles under real driving conditions.