• Energy Research
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Abstract This paper investigates the forced convection of alumina-water nanofluids within helical tubes, maintaining a constant wall temperature and assuming thermal equilibrium between the nanoparticles and the base fluid. The nanofluid model incorporates the effects of alumina (Al2O3) nanoparticle volume fraction, diameter, and temperature on thermophysical properties. The governing equations are solved using the Forward-Time Central-Space Finite Volume method in conjunction with the simple algorithm. Numerical results are validated against experimental data, demonstrating high accuracy. The study explores the effects of pitch size, curvature ratio, nanoparticle volume fraction, nanoparticle diameter, and Reynolds number on velocity contours, temperature profiles, secondary flow, thermophysical properties, friction coefficient, and heat transfer rate. Additionally, the figure of merit evaluates the impact of these parameters on the thermal performance of the system. The results indicate that an increase in Reynolds number and nanoparticle diameter negatively affects thermal performance, while higher nanoparticle volume fraction, curvature ratio, and pitch size enhance it. Furthermore, incorporating nanoparticles in straight tubes proves to be more advantageous compared to helical tubes. This study tested volumetric ratios of 1%, 2%, and 4%, which resulted in increases in heat transfer coefficients of 21%, 32%, and 43%, respectively, compared to pure water under similar conditions, such as Reynolds number and coil pitch.

Large amounts of steel slag (SS) and CO2 are produced globally each year during steel production. An SS-based carbon capture and utilization (SS-CCU) process for CO2 mineralization is suitable specifically for steel-making industries for simultaneous mitigation of CO2 emissions and valorization of wastes. However, the SS-CCU process is currently in the stage of laboratory research and far away from industrial application. In this review, some SS-CCU processes, including direct and indirect carbonation processes, were explored and summarized. Herein, the key factors and mechanisms of the SS-based CO2 sequestration process were identified. The carbonation process efficacy and its environmental impact (including global warming, energy use, water use, and metallic pollutants) were evaluated. Furthermore, the challenges and prospects of the further development of the SS-CCU process were discussed.

This study links sorghum biomass characteristics, such as lignocellulosic composition and morphology, to carbon properties like surface area, porosity, and electrochemical performance. Cellulose enrichment resulted in porous graphitic carbons with enhanced capacitance.

The recently completed Horizon-2020 project “Management and Uncertainties of Severe Accidents (MUSA)” has reviewed uncertainty sources and Uncertainty Quantification methodology for assessing Severe Accidents (SA), and has made a substantial effort at stimulating uncertainty applications in predicting the radiological Source Term of reactor and Spent Fuel Pool accident scenarios. The key motivation of the project has been to bring the advantages of the Best Estimate Plus Uncertainty approach to the field of Severe Accident modelling. With respect to deterministic analyses, expected gains are avoiding adopting conservative assumptions, identifying uncertainty bands of estimates, and gaining insights into dominating uncertain parameters. Also, the benefits for understanding and improving Accident Management were to be explored. The reactor applications brought together a large group of participants that set out to apply uncertainty analysis (UA) within their field of SA modelling expertise – in particular reactor types, but also SA code used (ASTEC, MELCOR, MAAP, RELAP/SCDAPSIM), uncertainty quantification tools used (DAKOTA, SUSA, URANIE, self-developed tools based on Python code), detailed accident scenarios, and in some cases SAM actions. The setting up of the analyses, challenges faced during that phase, and solutions explored, are described in Brumm et al. ANE 191 (2023). This paper synthesizes the reactor-application work at the end of the project. Analyses of 23 partners are presented in different categories, depending on whether their main goal is/are (i) uncertainty bands of simulation results; (ii) the understanding of dominating uncertainties in specific sub-models of the SA code; (iii) improving the understanding of specific accident scenarios, with or without the application of SAM actions; or, (iv) a demonstration of the tools used and developed, and of the capability to carry out an uncertainty analysis in the presence of the challenges faced. A cross-section of the partners’ results is presented and briefly discussed, to provide an overview of the work done, and to encourage accessing and studying the project deliverables that are open to the public. Furthermore, the partners’ experiences made during the project have been evaluated and are presented as good practice recommendations. The paper ends with conclusions on the level of readiness of UA in SA modelling, on the determination of governing uncertainties, and on the analysis of SAM actions.

Predict+Optimize frameworks integrate forecasting and optimization to address real-world challenges such as renewable energy scheduling, where variability and uncertainty are critical factors. This paper benchmarks solutions from the IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling, focusing on forecasting renewable production and demand and optimizing energy cost. The competition attracted 49 participants in total. The top-ranked method employed stochastic optimization using LightGBM ensembles, and achieved at least a 2% reduction in energy costs compared to deterministic approaches, demonstrating that the most accurate point forecast does not necessarily guarantee the best performance in downstream optimization. The published data and problem setting establish a benchmark for further research into integrated forecasting-optimization methods for energy systems, highlighting the importance of considering forecast uncertainty in optimization models to achieve cost-effective and reliable energy management. The novelty of this work lies in its comprehensive evaluation of Predict+Optimize methodologies applied to a real-world renewable energy scheduling problem, providing insights into the scalability, generalizability, and effectiveness of the proposed solutions. Potential applications extend beyond energy systems to any domain requiring integrated forecasting and optimization, such as supply chain management, transportation planning, and financial portfolio optimization.

Energy grids and markets are in transition. Increased use of renewable energy sources (RES) introduces new stability challenges for power grids. Despite the substantial electrical consumption of mobile networks, they are yet to harness their inherent flexibility for aiding in the stability of the power grid. A noticeable research gap exists concerning measuring full activation time for fast frequency reserve (FFR) product while using batteries from mobile network base stations. Our objective is to demonstrate that mobile operators could use their existing infrastructure to participate in the reserve market of a contemporary power grid. Furthermore, it seeks to determine if the full activation time can meet the requirements of an FFR product. The system consists of a live mobile base station site with a mobile connection to the site, local controller, an existing battery, and a power system that, in combination, can function as part of a power grid balancing system. Our main finding indicates that the rectifier reaction time within an installed base station site infrastructure ranges from 5 to 8 s, and the time when the base station is entirely off from the grid varies from 7 to 10 s. This finding is significant since the activation time is too long for the base station power system controller to be used for FFR. The required full activation time for FFR is less than 1.3 s. In conclusion, power system vendors should investigate improvements for their equipment and software products to enable fast reserve market entry for their existing customers and stay competitive.

All solid-state sodium metal batteries (ASSSMBs) have emerged as promising candidates in large-scale energy storage systems relative to Li/Na-ion batteries using flammable liquid electrolytes.