Search Results (1,259)

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

This work presents a high-fidelity simulation of the Peach Bottom turbine trip (PBTT) benchmark using the Virtual Environment for Reactor Applications (VERA), a multiphysics reactor modeling tool developed by the U.S. Department of Energy’s Consortium for Advanced Simulation of Light Water Reactors energy innovation hub. The PBTT benchmark, based on a 1977 transient event at the end of cycle 2 in a General Electric Type-4 boiling water reactor (BWR), is a critical test case for validating core physics models with thermal feedback during rapid reactivity events. VERA was employed to perform end-to-end, pin-resolved simulations from conditions at the beginning of cycle 1 through the turbine-trip transient, incorporating detailed neutron transport, fuel depletion, and subchannel thermal hydraulics. The simulation reproduced key benchmark observables with high accuracy: the peak power excursion occurred at 0.75 s, matching the scram time and closely aligning with the benchmark average of 0.742 s; the simulated maximum power spike was approximately 7600 MW, which is within 3% of the benchmark average of 7400 MW; and void-collapse dynamics were consistent with benchmark expectations. Reactivity predictions during cycles 1 and 2 remained within 1500 pcm and 400 pcm of criticality, respectively. These results confirm VERA’s ability to model complex coupled neutronic and thermal hydraulic behavior in a BWR turbine-trip transient, which will support its use in future studies of modeling dryout, fuel performance, and uncertainty quantification for transients of this type. Full article

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

Nuclear energy can help stop climate change by generating large amounts of emission-free electricity. Nuclear reactor designs are continually being developed to be more fuel efficient, safer, easier to construct, and to produce less nuclear waste. The term advanced nuclear reactors refers either to Generation III+ and Generation IV or small modular reactors. Every reactor is associated with the nuclear fuel cycle that must be economically viable and competitive. An important matter is optimization of fissile materials used in reactor and/or reprocessing of spent fuel and reuse. Currently operating reactors use the open cycle or partially closed cycle. Generation IV reactors are intended to play a significant role in reaching a fully closed cycle. At the same time, we can observe the growing interest in development of small modular reactors worldwide. SMRs can adopt either fuel cycle; they can be flexible depending on their design and fuel type. Spent nuclear fuel management should be an integral part of the development of new reactors. The proper management methods of the radioactive waste and spent fuel should be considered at an early stage of construction. The aim of this paper is to highlight the challenges related to reprocessing of new forms of nuclear fuel. Full article

Liquid crystal elastomers (LCEs) have shown great potential in the field of soft robotics due to their unique actuation capabilities. Despite the growing number of experimental studies in the soft robotics field, theoretical research remains limited. In this paper, a dynamic model of a bionic arm using an LCE fiber as artificial muscle is established, which exhibits periodic oscillation controlled by periodic illumination. Based on the assumption of linear damping and angular momentum theorem, the dynamics equation of the model oscillation is derived. Then, based on the assumption of linear elasticity model, the periodic spring force of the fiber is given. Subsequently, the evolution equations for the cis number fraction within the fiber are developed, and consequently, the analytical solution for the light-excited strain is derived. Following that, the dynamics equation is numerically solved, and the mechanism of the controllable oscillation is elucidated. Numerical calculations show that the stable oscillation period of the bionic arm depends on the illumination period. When the illumination period aligns with the natural period of the bionic arm, the resonance is formed and the amplitude is the largest. Additionally, the effects of various parameters on forced oscillation are analyzed. The results of numerical studies on the bionic arm can provide theoretical support for the design of micro-machines, bionic devices, soft robots, biomedical devices, and energy harvesters. Full article

Sanicro 25 (X7NiCrWCuCoNb25-23-3-3-2) steel is specifically designed for use in superheater components within the latest generation of conventional power plants. These power plants operate under conditions often referred to as super-ultra-supercritical, with steam parameters that can reach up to 30 MPa and temperatures of 653 °C for fresh steam and 672 °C for reheated steam. While last-generation supercritical power plants still rely on fossil fuels, they represent a significant step forward in more sustainable energy production. The most sophisticated facilities of this kind can achieve thermodynamic efficiencies exceeding 47%. This study aimed to conduct a detailed analysis of the initial precipitation processes occurring in Sanicro 25 steel within the temperature range of 700–900 °C. The temperature of 700 °C corresponds to the operational conditions of this material, particularly in secondary steam superheaters in thermal power plants that operate under ultra-supercritical parameters. Understanding precipitation processes is crucial for optimizing mechanical performance, particularly in terms of long-term strength and creep resistance. To accurately assess the microstructural changes that occur during the early stages of service, a digital twin approach was employed, which included CALPHAD simulations and experimental heat treatments. Experimental annealing tests were conducted in air within the temperature range of 700–900 °C. Precipitation behavior was simulated using the Thermo-Calc 2025a with Dictra software package. The results from Prisma simulations correlated well with the experimental data related to the kinetics of phase transformations; however, it was noted that the predicted sizes of the precipitates were generally smaller than those observed in experiments. Additionally, computational limitations were encountered during some simulations due to the complexity arising from the numerous alloying elements present in Sanicro 25 steel. The microstructural evolution was investigated using various methods, including light microscopy (LM), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Full article

In light of the persistent environmental degradation driven by fossil fuels, developing new energy sources is essential for achieving sustainability. The recent surge in electric vehicle adoption has underscored the significance of new energy batteries. However, the supply chains of new energy battery manufacturers face multiple sustainability risks, which impede sustainable practice adoption. To tackle these challenges, leanness philosophy is an effective tool, and Industry 5.0 enhances its efficacy significantly, further mitigating sustainability risks. This study integrates the supply chain, leanness philosophy, and Industry 5.0 by applying quality function deployment. A novel four-phase hybrid MCDM model integrating the fuzzy Delphi method, DEMATEL, AHP, and fuzzy VIKOR, identified five key sustainability risks five core leanness principles, and eight critical Industry 5.0 enablers. By examining a Chinese new energy battery manufacturer as a case study, the findings aim to assist managers and decision-makers in mitigating sustainability risks within their supply chains. Full article

Recently, solar energy technologies are a cornerstone of the global effort to transition towards cleaner and more sustainable energy systems. However, in many rural areas of developing countries, unreliable electricity severely impacts healthcare delivery, resulting in reduced medical efficiency and increased risks to patient safety. This review explores the transformative potential of solar energy as a sustainable solution for powering healthcare facilities, reducing dependence on fossil fuels, and improving health outcomes. Consequently, energy harvesting is a vital renewable energy source that captures abundant solar and thermal energy, which can sustain medical centers by ensuring the continuous operation of life-saving equipment, lighting, vaccine refrigeration, sanitation, and waste management. Beyond healthcare, it reduces greenhouse gas emissions, lowers operational costs, and enhances community resilience. To address this issue, the paper reviews critical solar energy technologies, energy storage systems, challenges of energy access, and successful solar energy implementations in rural healthcare systems, providing strategic recommendations to overcome adoption challenges. To fulfill the aims of this study, a focused literature review was conducted, covering publications from 2005 to 2025 in the Scopus, ScienceDirect, MDPI, and Google Scholar databases. With targeted investments, policy support, and community engagement, solar energy can significantly improve healthcare access in underserved regions and contribute to sustainable development. Full article

The biomass-to-liquid process is a promising alternative for sustainably meeting the growing demand for liquid fuels. This study focuses on the fabrication, characterization, and performance of a structured iron catalyst for producing hydrocarbons through Fischer–Tropsch synthesis (FTS). The catalyst was designed to address some drawbacks of conventional supported catalysts, such as low utilization, poor activity, and instability. The experimental investigation involved the manufacturing and characterization of both promoted and unpromoted iron-based catalysts. The performance of the structured iron catalyst was assessed in a fixed-bed reactor under relevant industrial conditions. Notably, the best results were achieved with a syngas ratio typical of the gasification of lignocellulosic biomass, where the catalyst exhibited superior catalytic activity and selectivity toward desired hydrocarbon products, including light olefins and long-chain paraffins. The resulting structured catalyst achieved up to 95% CO conversion in a single pass with 5% selectivity for CH₄. The results indicate that the developed structured iron catalyst has considerable potential for efficient and sustainable hydrocarbon production via the Fischer–Tropsch synthesis. The catalyst’s performance, enhanced stability, and selectivity present promising opportunities for its application in large-scale hydrocarbon synthesis processes. Full article

Many environmental risks, such as global warming and depletion of natural resources, force governments to achieve economic growth and financial development without causing environmental degradation. The dependency of countries’ dependence on fossil fuels also causes energy supply security problems due to the associated risks at regional and global levels. These reasons lead countries to diversify and increase their renewable energy investments. In this context, this study focuses on the most attractive countries in terms of renewable energy investments and analyzes the relationships between renewable energy consumption (REC), carbon dioxide emissions (CO₂), economic growth (EGRO), financial development (FD), and energy dependence (EDP) using the panel regression method. This research uses data from 38 countries between 1991 and 2021 within the scope of the “Renewable Energy Attractiveness Index” (RECAI) created by Ernst & Young. As a result of the heterogeneity and cross-sectional dependency tests, the data were analyzed using the Westerlund cointegration test, the Augmented Mean Group (AMG) estimator, and the Emirmahmutoglu and Kose causality test. The findings from this study show that FD and EGRO have a positive and significant effect on REC, whereas they have a negative and significant relationship with CO₂ emissions. Findings from the causality test show that FD has an impact on both CO₂ and EGRO. In addition, within the scope of this study, a causality was determined between EDP and REC, and a mutual relationship between energy demand and CO₂ was revealed. In light of these findings, governments should increase their investments in renewable energy to ensure sustainable economic growth and energy supply security while minimizing environmental degradation. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive disease that degeneratively damages both upper and lower motor neurons, eventually resulting in muscular paralysis and death. Although ALS is broad in scope and commonly thought of as a motor neuron disease, more active research sheds light on the that role skeletal muscle plays in the development and progression of the disease. Muscle tissue in ALS patients and in animal models demonstrates severe regenerative deficits, including impaired myogenesis and impaired myoblast fusion. In ALS, muscle stem cells, known as satellite cells, show poor performance in activation, proliferation, and differentiation and thus contribute to ALS muscle wasting. Moreover, the pathological tissue environment that inhibits myoblast fusion is made up of proinflammatory cytokines, oxidative stress, and a lack of trophic signals from the neuromuscular junction, which greatly disrupts homeostatic regulation. It is likely that skeletal muscle is instead a dynamic player, fueling neuromuscular degeneration as opposed to a passive responder to denervation. One must appreciate the cellular and molecular changes that complicate muscle regeneration in ALS for effective treatment to be developed, permitting simultaneous interventions with both muscle and neurons. Full article

Residual dry matter (RDM) is a term used in rangeland management to describe the non-photosynthetic plant material left on the soil surface at the end of the growing season. RDM measurements are used by agencies and conservation entities for managing grazing and fire fuels. Measuring the RDM using traditional methods is labor-intensive, costly, and subjective, making consistent sampling challenging. Previous studies have assessed the use of multispectral remote sensing to estimate the RDM, but with limited success across space and time. The existing approaches may be improved through the use of spectroscopic (hyperspectral) sensors, capable of capturing the cellulose and lignin present in dry grass, as well as Unmanned Aerial Vehicle (UAV)-mounted Light Detection and Ranging (LiDAR) sensors, capable of capturing centimeter-scale 3D vegetation structures. Here, we evaluate the relationships between the RDM and spectral and LiDAR data across the Jack and Laura Dangermond Preserve (Santa Barbara County, CA, USA), which uses grazing and prescribed fire for rangeland management. The spectral indices did not correlate with the RDM (R² < 0.1), likely due to complete areal coverage with dense grass. The LiDAR canopy height models performed better for all the samples (R² = 0.37), with much stronger performance (R² = 0.81) when using a stratified model to predict the RDM in plots with predominantly standing (as opposed to laying) vegetation. This study demonstrates the potential of UAV LiDAR for direct RDM quantification where vegetation is standing upright, which could help improve RDM mapping and management for rangelands in California and beyond. Full article

It has been proven that the performance of fluid catalytic cracking (FCC), as the most important oil refining process for converting low-value heavy oils into high-value transportation fuels, light olefins, and feedstocks for petrochemicals, depends strongly on the quality of the feedstock. For this reason, characterization of feedstocks and their relationships to FCC performance are issues deserving special attention. This study systematically reviews various publications dealing with the influence of feedstock characteristics on FCC performance, with the aim of identifying the best characteristic descriptors allowing prediction of FCC feedstock cracking capability. These characteristics were obtained by mass spectrometry, SARA analysis, elemental analysis, and various empirical methods. This study also reviews published research dedicated to the catalytic cracking of biomass and waste oils, as well as blends of petroleum-derived feedstocks with sustainable oils, with the aim of searching for quantitative relationships allowing prediction of FCC performance during co-processing. Correlation analysis of the various FCC feed characteristics was carried out, and regression techniques were used to develop correlations predicting the conversion at maximum gasoline yield and that obtained under constant operating conditions. Artificial neural network (ANN) analysis and nonlinear regression techniques were applied to predict FCC conversion from feed characteristics at maximum gasoline yield, with the aim of distinguishing which technique provided the more accurate model. It was found that the correlation developed in this work based on the empirically determined aromatic carbon content according to the n-d-M method and the hydrogen content calculated via the Dhulesia correlation demonstrated highly accurate calculation of conversion at maximum gasoline yield (standard error of 1.3%) compared with that based on the gasoline precursor content determined by mass spectrometry (standard error of 1.5%). Using other data from 88 FCC feedstocks characterized by hydrogen content, saturates, aromatics, and polars contents to develop the ANN model and the nonlinear regression model, it was found that the ANN model demonstrated more accurate prediction of conversion at maximum gasoline yield, with a standard error of 1.4% versus 2.3% for the nonlinear regression model. During the co-processing of petroleum-derived feedstocks with sustainable oils, it was observed that FCC conversion and yields may obey the linear mixing rule or synergism, leading to higher yields of desirable products than those calculated according to the linear mixing rule. The exact reason for this observation has not yet been thoroughly investigated. Full article

Pulse and Glide (PnG), as a fuel-saving technique, has primarily been applied to manual transmission vehicles. So, its effectiveness when integrated with a novel vehicle type like connected and automated vehicles (CAVs) remains largely unexplored. On the other hand, CAVs have evidently received less attention regarding energy conservation, and their prominent perception capabilities clearly exhibit individual variations. In light of this, this study investigates the impacts of PnG combined with CAVs on energy conservation and safety within the emerging mixed traffic flow composed of CAVs with varying sensing accuracies. The results indicate the following: (i) compared to the traditional driving modes, the PnG can achieve a maximum fuel-saving rate of 39.53% at Fuel Consumption with Idle (FCI), reducing conflicts by approximately 30% on average; (ii) CAVs, equipped with sensors boasting a greater detection range, markedly enhance safety during vehicle operation and contribute to a more uniform distribution of individual fuel consumption; (iii) PnG modes with moderate acceleration, such as 1–2 m/s², can achieve excellent fuel consumption while ensuring safety and may even slightly enhance the operational efficiency of the intersection. The findings could provide a theoretical reference for the transition of transportation systems toward sustainability. Full article

The active safety of tractors remains a major concern in rural road environments, where tractor drivers face high crash risks due to limited vehicle visibility. In Serbia, 1.4% of crashes involve tractors, mainly due to poor visibility (64.3%), lack of beacon lights, unsafe overtaking, and unmarked stopped tractors (14.3% each). These issues reduce safety, increase fuel consumption and emissions, and cause economic losses. A driving simulator study with 117 drivers examined how visibility equipment affects speed perception. The results showed that 20 km/h was best estimated with all visibility aids, while 10 km/h was most accurately judged with only the slow-moving vehicle emblem. These findings emphasize the potential for simple, cost-effective visibility measures to enhance the active safety of tractors in mixed rural traffic conditions. By enhancing tractor visibility, these measures reduce crash risks, minimize unnecessary acceleration and deceleration, and lower fuel consumption and emissions associated with traffic disturbances. Furthermore, by preventing crashes, these solutions contribute to reducing resource consumption in crash-related medical care, vehicle repairs, and infrastructure damage. Integrating improved visibility equipment into rural traffic policy can significantly enhance tractors’ active safety and reduce the risk of crashes in agricultural regions. Full article