Search Results (533)

Ensuring the overall efficiency of hydraulic fracturing treatment depends on the ability to forecast bottomhole pressure. It has a direct impact on fracture geometry, production efficiency, and cost control. Since the complications present in contemporary operations have proven insufficient to overcome inherent uncertainty, the precision of bottomhole pressure predictions is of great importance. Achieving this objective is possible by employing machine learning algorithms that enable real-time forecasting of bottomhole pressure. The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. Using a large body of work, including 42 vertical wells, an extensive dataset was constructed and meticulously packed using processes such as feature selection and data manipulation. Eleven machine learning models were then developed using parameters typically available during hydraulic fracturing operations as input variables, including surface pressure, slurry flow rate, surface proppant concentration, tubing inside diameter, pressure gauge depth, gel load, proppant size, and specific gravity. These models were trained using actual bottomhole pressure data (measured) from deployed memory gauges. For this study, we carefully developed machine learning algorithms such as gradient boosting, AdaBoost, random forest, support vector machines, decision trees, k-nearest neighbor, linear regression, neural networks, and stochastic gradient descent. The MSE and R² values of the best-performing machine learning predictors, primarily gradient boosting, decision trees, and neural network (L-BFGS) models, demonstrate a very low MSE value and high R² correlation coefficients when mapping the predictions of bottomhole pressure to actual downhole gauge measurements. R² values are reported as 0.931, 0.903, and 0.901, and MSE values are reported at 0.003, 0.004, and 0.004, respectively. Such low MSE values together with high R² values demonstrate the exceptionally high accuracy of the developed models. By illustrating how machine learning models for predicting pressure can act as a viable alternative to expensive downhole pressure gauges and the inaccuracy of conventional models and correlations, this work provides novel insight. Additionally, machine learning models excel over traditional models because they can accommodate a diverse set of cross-linked fracture fluid systems, proppant specifications, and tubing configurations that have previously been intractable within a single conventional correlation or model. Full article

The transition to clean energy has accelerated the pursuit of hydrogen as a sustainable fuel. Among various production methods, proton exchange membrane electrolysis cells (PEMECs) stand out due to their ability to generate ultra-pure hydrogen with efficiencies exceeding 80% and current densities reaching 2 A/cm². Their compact design and rapid response to dynamic energy inputs make them ideal for integration with renewable energy sources. This review provides a comprehensive assessment of PEMEC technology, covering key internal components, system configurations, and efficiency improvements. The role of catalyst optimization, membrane advancements, and electrode architectures in enhancing performance is critically analyzed. Additionally, we examine state-of-the-art numerical modelling, comparing zero-dimensional to three-dimensional simulations and single-phase to two-phase flow dynamics. The impact of oxygen evolution and bubble dynamics on mass transport and performance is highlighted. Recent studies indicate that optimized electrode architectures can enhance mass transport efficiency by up to 20%, significantly improving PEMEC operation. Advancements in two-phase flow simulations are crucial for capturing multiphase transport effects, such as phase separation, electrolyte transport, and membrane hydration. However, challenges persist, including high catalyst costs, durability concerns, and scalable system designs. To address these, this review explores non-precious metal catalysts, nanostructured membranes, and machine-learning-assisted simulations, which have demonstrated cost reductions of up to 50% while maintaining electrochemical performance. Future research should integrate experimental validation with computational modelling to improve predictive accuracy and real-world performance. Addressing system control strategies for stable PEMEC operation under variable renewable energy conditions is essential for large-scale deployment. This review serves as a roadmap for future research, guiding the development of more efficient, durable, and economically viable PEM electrolyzers for green hydrogen production. Full article

The increasing demand for sustainable road infrastructure necessitates alternative materials that enhance soil stabilization while reducing environmental impact. This study investigated the application of organosilane-based nanotechnology to improve the structural performance and durability of road corridors in Peru, offering a viable alternative to conventional stabilization methods. A comparative experimental approach was employed, where modified soil and asphalt mixtures were evaluated against control samples without nanotechnology. Laboratory tests showed that organosilane-treated soil achieved up to a 100% increase in the California Bearing Ratio (CBR), while maintaining expansion below 0.5%, significantly reducing moisture susceptibility compared to untreated soil. Asphalt mixtures incorporating nanotechnology-based adhesion enhancers exhibited a Tensile Strength Ratio (TSR) exceeding 80%, ensuring a superior resistance to moisture-induced damage relative to conventional mixtures. Non-destructive evaluations, including Dynamic Cone Penetrometer (DCP) and Pavement Condition Index (PCI) tests, confirmed the improved long-term durability and load-bearing capacity. Furthermore, statistical analysis of the International Roughness Index (IRI) revealed a mean value of 2.449 m/km, which is well below the Peruvian regulatory threshold of 3.5 m/km, demonstrating a significant improvement over untreated pavements. Furthermore, a comparative reference to IRI standards from other countries contextualized these results. This research underscores the potential of nanotechnology to enhance pavement resilience, optimize resource utilization, and advance sustainable construction practices. Full article

The aim of this work is to evaluate the influence of the surface deformations of an open inflatable wing section on aerodynamic performance and boundary layer separation phenomena. The inflation/deflation processes are allowed by an air intake placed on the bottom side of the model. Due to its low rigidity, non-contact measurements are required. Therefore, an infrared thermography technique was applied in order to detect local surface deformations and local separation phenomena. Additionally, the inflation and deflation of the whole wing were studied through an innovative approach, introduced by the authors, based on a piezoelectric sensor. It is important to note that open and closed wing sections exhibit very different aerodynamic behavior. For these reasons, both cases were investigated in the following research. The impact of deformation on the wing’s aerodynamic performance was assessed by means of wind tunnel tests. The inflatable wing presented lower lift and higher drag than the corresponding rigid wing due to the fabric’s deformations. Furthermore, the lift and moment coefficient curves were strongly related to the wing’s inflation. In particular, there was a change in the slope of the lift curve and a drop in the moment coefficient when the wing inflated. Lastly, the results provided evidence that a thermographic approach can be used to qualitatively detect local deformations of an inflatable wing and that a piezoelectric sensor can be used feasibly in detecting the inflation and deflation phases of a wing. Full article

The United Kingdom seeks to achieve net-zero emissions by 2050, mostly via the shift to an electrical system exclusively powered by zero-carbon sources. Microgrids (MGs) can be seen as an effective system for integrating renewables into the energy portfolio. Nonetheless, MGs face the acknowledged obstacle of backup power generation due to the intermittent nature of renewable energy sources, necessitating the establishment of backup power generation capacity. This paper contrasts selfish power generation, where the MG pursues complete energy autonomy, with an alternative influenced by lean principles (Heijunka production), which seeks to stabilise power transactions within the national electricity supply chain, reduce emissions, and tackle the backup generation challenge. This study proposes a pre-contractual order update (COU) strategy for the operation of hybrid collaborative MG where a forward order update to the utility grid is placed, in contrast to selfish MG, which uses a spot order update strategy. The COU strategy was defined, and two simulation models (for selfish and collaborative MG) were developed, each incorporating four backup generation scenarios to illustrate the method’s efficacy by assessing the system’s critical performance metrics. It has been found that the collaborative MG model reduced the carbon emissions by 62% and the volatility of unplanned orders to the grid by 61% compared to the selfish model in the first scenario (grid-dependent MG). Furthermore, the MG achieved zero volatility and a 33% reduction in carbon content in the collaborative MG when using the H₂ burner as backup generation compared to the first scenario. Indicating that sustainability encompasses not only the use of renewable resources but also the stability of their outputs through the implementation of collaborative MGs. Full article

The aviation sector faces a significant challenge in balancing the rising demand for air travel with the need to reduce its environmental impact. Because air travel accounts for approximately 2.5% of global carbon emissions, there is a need to find sustainable solutions to reduce its environmental impact. Improving aerodynamic performance is a crucial area for reducing fuel consumption and emissions. Nowadays, more focus is given to commercial aviation, which contributes to global aviation emissions. The A380 is the largest passenger aircraft in the world at the moment. It was observed in real life that the wake turbulence from the A380 led to a sudden loss of the Challenger aircraft’s control and a rapid descent of more than 10,000 feet. This Challenger incident is a wake-up call to address the A380’s wake turbulence. Hence, this research focuses on designing and analysing blended winglets for the Airbus A380 to reduce wake turbulence. With the use of modern computational fluid dynamics tools, the current A380 winglets’ performance was evaluated to identify the level of lift, drag and wake vortex patterns. To address these challenges, the performance of newly designed blended winglets with different cant angles, i.e., 0, 15, 45 and 80, was analysed computationally using the K-ω SST turbulent model in the software ANSYS Fluent 2024 R1. It resulted in a decrease in the wake vortex size accompanied by a 1.724% decrease in drag. This research project evidenced that addressing the wake turbulence issue on a large aircraft could improve aerodynamic performance and thus contribute towards sustainable aviation. Full article

Scaling in geothermal production wells poses a critical challenge to sustainable energy production, particularly in vapor-dominated systems where scaling mechanisms are less understood. This study investigates scale treatment planning using the broaching method in Well X at Indonesia’s Kamojang geothermal field. Through well integrity testing, geochemical analysis, and XRD characterization, silica (quartz) scale formations were identified in the production casing. Performance monitoring revealed gradual decreases in steam production and wellhead pressure over a three-year period. The selection of the broaching method was validated through analysis of scale characteristics, well geometry, and economic feasibility, offering a significantly more cost-effective solution compared to conventional methods with a substantially shorter payback period. Broaching has effectively operated on multiple geothermal wells, restoring significant production capacity at approximately half the expense of conventional well workover methods. Our results challenge accepted assumptions on scaling in vapor-dominated systems and provide a methodical framework for scale treatment planning. This study demonstrates how strategic scale management can efficiently preserve well productivity while lowering operating costs, thus enabling sustainable geothermal resource development for operators worldwide. Full article

This review paper investigates the comprehensive impact of various nanomaterials on the mechanical properties and durability of pavement-quality concrete (PQC) with a specific focus on compressive strength, flexural strength, split tensile strength, permeability, abrasion resistance, fatigue performance, and crack relief performance. Despite significant advancements in the use of nanomaterials in concrete, existing research lacks a comprehensive evaluation of their comparative effectiveness, optimal dosages, and long-term durability in PQC. While conventional PQC faces challenges such as low fatigue resistance, high permeability, and susceptibility to abrasion, studies on nanomaterials have largely focused on individual properties rather than a holistic assessment of their impact. Nano SiO₂ and graphene oxide (GO) emerged as the most effective, with optimal dosages of 2% and 0.03%, respectively, leading to substantial improvements in compressive strength (up to 48.88%), flexural strength (up to 60.7%), and split tensile strength (up to 78.6%) through improved particle packing, reduced permeability, and refined microstructure. Nano TiO₂, particularly at a 1% dosage, significantly enhanced multiple properties, including a 36.30% increase in compressive strength, over 100% improvement in abrasion resistance, and a 475% increase in fatigue performance. However, a critical research gap exists in understanding the combined effects of multiple nanomaterials, their interaction mechanisms within cementitious systems, and their real-world performance under prolonged environmental and loading conditions. Most studies have been limited to laboratory-scale investigations, with minimal large-scale validation for pavement applications. The findings indicate that nanomaterials like nano TiO₂, nano CaCO₃, nano Al₂O₃, nano clay, and carbon nanomaterials play crucial roles in improving characteristics like permeability, abrasion resistance, and fatigue performance, with notable gains observed in many cases. This review systematically analyzes the influence of these nanomaterials on PQC, identifies key research gaps, and emphasizes the need for large-scale field validation to enhance their practical applicability. Full article

Soil controls water distribution, which is crucial for accurate hydrological modeling. MOHID-Land is a physically based, spatially distributed model that uses van Genuchten–Mualem (VGM) functions to calculate water content in porous media. The hydraulic soil parameters of VGM are dependent on soil type and are typically estimated from experimental data; however, they are often obtained using pedotransfer functions, which carry significant uncertainty. As a result, calibration is frequently required to account for both the natural spatial variability of soil and uncertainties estimation. This study focuses on a representative Atlantic Forest watershed. It assesses the sensitivity of channel flow to VGM parameters using a mathematical approach based on residuals derivative, aimed at enhancing soil calibration efficiency for MOHID-Land. The model’s performance significantly improved following calibration, considering only five parameters. The NSE improved from 0.16 on the base simulation to 0.53 after calibration. A sensitivity analysis indicated the curve adjustment parameter ($n$) as the most sensitive parameter, followed by saturated water content (${\theta }_s$) considering the 10% variation. Additionally, a combined change in ${\theta }_s$, $n$, residual water content (${\theta }_r$), curve adjustment parameter ($\alpha$), and saturated conductivity ($K_{sat}$) values by 10% significantly improves the model’s performance, by reducing channel flow peaks and increasing baseflow. Full article

Online monitoring of partial discharge (PD) is a complex task traditionally requiring specialized expertise. However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. The proposed method employs a findpeaks algorithm based on Fast Fourier Transform (FFT) to extract frequency key features, denoted as f₁ and f₂, from the frequency spectrum. These features are used to estimate model parameters for each PD type, enabling the representation of their frequency density distributions in a 2D map (f₁, f₂) via Gaussian Mixture Models (GMMs). The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. For PD identification, log-likelihood and softmax functions are applied, achieving an evaluation accuracy of 96.68%. The model also demonstrates robust performance in identifying unknown PD data, with accuracy ranging from 78.10% to 95.11%. This approach enhances the distinction between PD types based on their frequency characteristics, providing a reliable tool for PD signal analysis and identification. Full article

Optimizing agricultural tire traction is essential for improving field efficiency and minimizing soil degradation. This study examines the influence of lug spacing and vertical load on traction performance using Finite Element Analysis (FEA) in ANSYS and the semi-empirical Wong and Preston-Thomas tire model. Simulations were conducted on clay soil under vertical loads of 35 kN, 45 kN, and 55 kN, with varying lug spacings. The results indicate that a 130 mm lug spacing provides the best balance between traction, thrust, and motion resistance. Higher vertical loads intensify soil compaction, leading to reduced thrust generation at 55 kN despite decreased motion resistance. These findings emphasize the importance of optimizing lug configurations to enhance traction while mitigating soil compaction. The study contributes to improving tire designs for agricultural machinery, promoting efficiency and sustainability in soil management. Full article

Perovksite solar cells have emerged as a promising photovoltaic technology due to their high increasing power conversion efficiency (PCE). However, challenges related to thermal instability and material toxicity, especially in lead-based perovskites, bring the need to investigate alternative materials and structural designs. This study investigated the current–voltage and power–voltage characteristics of lead-free PSCs based on tin- and germanium using a two-diode equivalent circuit model. The novelty of this work was based on the intensive evaluation of three different electron transport layers (ETLs)—titanium dioxide (TiO₂), zinc oxide (ZnO), and tungsten trioxide (WO₃)—under different ambient temperature conditions (5 °C, 25 °C, and 55 °C) to study their impacts on device performance and the thermal stability. SCAPS-1D simulations were used to model the electrical and optical behaviors of the proposed perovskite structures, and the results were validated by using the two-diode model. The main performance parameters that were considered were open-circuit voltage, short-circuit current, maximum power point, and fill factor. The results showed that TiO₂ was better than ZnO and WO₃ as an ETL, achieving a PCE of 24.83% for Sn-based perovskites, and ZnO was the better choice for Ge-based perovskites at 25 °C, with an efficiency reaching ~15.39%. The three ETL materials showed high thermal stability when analyzing them at high ambient temperatures reaching 55 °C. Full article

The advent of small modular reactors (SMRs) represents a transformative leap in nuclear technology. With their smaller size, modular construction, and safety features, SMRs address challenges faced by traditional reactors. However, these technological advancements pose significant regulatory challenges that must be addressed to ensure their safe and effective integration into the energy grid. This paper presents robust regulatory strategies essential for the deployment of SMRs. We also perform economic and sensitivity analysis on a notional SMR project to assess its feasibility, profitability, and long-term viability, pinpointing areas for cost optimization and determining the project’s resilience to market trends and technological changes. Key findings highlight market demand as the most influential factor, with public acceptance, regulatory clarity, economic viability, and government support playing critical roles. The sensitivity analysis shows that SMRs could account for 3% to 9% of the energy market by 2050, with a base case of 4.5%, emphasizing the need for coordinated efforts among policymakers, industry stakeholders, and regulatory bodies. Technological maturity suggests current designs are viable, with future R&D focusing on market appeal and safety. By synthesizing these insights, the paper aims to guide regulatory authorities in facilitating informed decision-making, policy formulation, and the adoption of SMRs. Full article

Glass fiber-reinforced polymer (GFRP) composites are widely utilized across industries, particularly in structural components exposed to hygrothermal environments characterized by elevated temperature and moisture. Such conditions can significantly degrade the mechanical properties and structural integrity of GFRP composites. Therefore, it is essential to utilize effective methods for assessing their hygrothermal aging. Traditional approaches to hygrothermal aging evaluation are hindered by several limitations, including time intensity, high costs, labor demands, and constraints on specimen size due to laboratory space. This study addresses these challenges by introducing a facile and efficient alternative that evaluates GFRP degradation under hygrothermal conditions through surface wettability analysis. Herein, a glass fiber-reinforced benzoxazine (BZ) composite was fabricated using the vacuum-assisted resin transfer molding (VARTM) method and was aged in a controlled humidity and temperature chamber for up to 5 weeks. When analyzing the wettability characteristics of the composite, notable changes in the contact angle (CA) and contact angle hysteresis (CAH) were 21.77% and 90.90%, respectively. Impact droplet dynamics further demonstrated reduced wetting length and faster droplet equilibrium times with the prolonged aging duration, indicating a progressive decline in surface characteristics. These changes correlated with reductions in flexural strength, highlighting the surface’s heightened sensitivity to environmental degradation compared with internal structural integrity. This study emphasizes the critical role of surface characterization in predicting the overall integrity of GFRP composites. Full article

Water body detection from satellite imagery is still challenging due to spectral confusion and the limitation of traditional water indices. This paper proposes a new approach by incorporating Particle Swarm Optimization with a Spectral Multi-Band Water Index for the enhanced detection of water bodies using Sentinel-2 imagery. The proposed approach optimizes the coefficients of seven Sentinel-2 bands (Blue, Green, NIR, NIR-Narrow, Water Vapor, SWIR1, and SWIR2) using an intelligent PSO with adaptive inertia weight and early stopping mechanisms. This work strategy proposes a new fitness function that applies dynamic thresholding and target-based optimization, allowing it to calibrate precisely to the local characteristics of the water body. The performance of the PSO-SMBWI was evaluated against traditional water indices, including the NDWI, MNDWI, and AWEI. The results indicate that the PSO-SMBWI has the highest accuracy, which exactly coincides with the ground truth of water coverage (12.12%), while the NDWI, MNDWI, and AWEI have deviations of +1.24%, +0.53%, and +12.15%, respectively. The proposed method automatically handles multi-resolution band integration in 10 m, 20 m, and 60 m and eliminates manual threshold tuning. Furthermore, our consensus-based validation approach ensures robust performance verification. Its effectiveness is due to its adaptive optimization framework and comprehensive spectral analysis. Hence, it is most suitable for any geographical context on the ground for highly accurate water body mapping. This research contributes a lot to the area of remote sensing by introducing an automated, highly accurate, and very computationally efficient approach to water body detection. Full article