Administrative Divisions Al-Mustaqbal Energy Research Center
Abstract In current work, simulations of the freezing process within a tank featuring plus sign-shaped fins were conducted utilizing the Galerkin method. The focus was on unsteady heat transfer based on conduction, solving two coupled equations. The primary material in this investigation is water, which is strategically combined with CuO nanoparticles to improve cold energy preservation efficiency. The simulations are influenced by two critical factors: the shape and concentration of the nanoparticles. Considering these factors adds complexity to the study, facilitating a comprehensive exploration of their impacts on solidification within the plus sign-shaped fin container. Validation of the code against previous benchmarks has shown good accuracy. For water alone, the cold storage time is 1433 s, which significantly reduces to 1049.19 s with the introduction of nanoparticles. The substantial decrement in freezing time is observed with the augmentation of both ϕ and “m” (shape factor), approximately 7 % and 27 %, respectively. https://www.sciencedirect.com/science/article/pii/ S2590123025002178
This research aims to address the critical need for sustainable cooling systems in greenhouses, particularly relevant in mitigating global warming impacts and enhancing food security worldwide. The urgency becomes more pronounced in locations experiencing high ambient temperature and humidity. The study introduces an innovative cooling system integrating Phase Change Material, a desiccant wheel, and an absorption chiller, powered by solar and biomass energy. This novel system aims to efficiently regulate temperature and humidity in greenhouse environments. The performance of this system is examined in Abu Dhabi, Doha, and Riyadh during the summer months, utilizing TRNSYS software for a medium-scale greenhouse model. Additionally, a comprehensive Life Cycle Assessment is carried out to quantify the environmental impacts of the proposed system. Results indicate that in Abu Dhabi, the system yields a Coefficient of Performance (COP) of 1.108, effectively maintaining indoor climate conditions. Similarly, Doha and Riyadh exhibit COPs of 1.015 and 0.827, respectively. In terms of solar energy utilization, Abu Dhabi demonstrates a solar fraction of 40.4, corresponding to the lowest Global Warming Potential (GWP) at 0.106 kg CO2eq per 1 kW of provided cooling capacity. Conversely, Riyadh records the highest GWP at 0.149 kg CO2eq, followed by Doha at 0.118 kg CO2eq. The Energy Payback Time (EPBT) for the system in Abu Dhabi is calculated to be 3.96 years, the shortest among the examined cities. In comparison, Doha and Riyadh present longer EPBTs of 4.48 and 5.83 years, respectively. These findings suggest that the proposed system offers a viable and environmentally friendly alternative to conventional greenhouse cooling approaches. https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24034571
There s a growing emphasis on adopting eco-friendly energy sources to mitigate greenhouse gas emissions and foster sustainability. While renewable energy sources like solar power offer numerous benefits, they have some limitations. Additionally, storing electricity generated from solar panels can be costly and challenging due to limitations in battery technology and storage capacity. Therefore, phase change material-based thermal energy storage systems offer a promising solution to these challenges. These systems also play a crucial role in enhancing buildings energy efficiency and sustainability. The dimensionality of these systems affects their performance. Traditional systems often rely on fixed dimensions, leading to non-uniform thermal conditions within the storage medium. To address these challenges, adopting a compact-latent heat storage (C-LHS) mechanism was recommended herein. Besides, some fins were inserted into the C-LHS system to alter the heat transfer dynamics within the device. Three of the critical parameters of the fins were changed to examine their impact on the charging time. Artificial neural networks and genetic algorithms were employed to determine the optimal positioning of fins to minimize the duration of material to get both part (melt fraction of 0.8) and full (melt fraction of 1) melting capacities. Here, the primary aim was to present a predictive model capable of accurately forecasting the charging time of the material. In all the presented finned samples, the entire PCM melted in less than 15,110 s (4 h and 12 min) and was able to fully utilize the energy absorption capacity in latent form, whereas in the non-finned system, only 68.6 % of the material managed to melt within the entire duration of the investigation (5 h). After analyzing the data, two optimal configurations of OS1 and OS2 with the minimum time for melt fractions of 0.8 and 1 were introduced. In the OS2 configuration, it took 16,200 s (4 h and 30 min) to absorb 4929 kJ of energy. The non-finned sample absorbed only 3250 kJ of energy during the same period. In General, OS2 achieved total energy absorption 51.6 % faster than the non-finned sample. Therefore, the introduced system has the potential to increase absorbed energy in the rest of the daylight hours to further amounts by increasing the number of units and employing a larger volume of material. https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24035527
This study offers an in-depth thermodynamic analysis and optimization of an integrated renewable energy system that merges a double-flash geothermal system with a transcritical carbon dioxide Rankine cycle, utilizing machine learning algorithms. The innovative design aims to maximize the concurrent generation of heat and electricity, ultimately benefiting environmental sustainability and energy security. By employing regression machine learning algorithms, the research evaluates and enhances system performance, achieving remarkable R-squared accuracy levels of 98.86 % for heating output and 99.89 % for power output predictions. The thermodynamic modeling, which has been validated against recognized benchmarks, confirms the accuracy of the system s design. Optimization findings indicate that operating pressures between 840 and 870 kPa and pressure ratios of 1.56–1.60 deliver optimal outputs, with power production between 2582 and 2585 kW and heating output ranging from 12260 to 12280 kW. The system reaches its maximum performance at a pressure of 850 kPa and a pressure ratio of 1.57, resulting in a power output of 2583.97 kW and a heating output of 12279.3 kW. These results highlight the potential of combining advanced thermodynamic systems with machine learning methodologies to improve the efficiency and effectiveness of renewable energy sources. https://www.sciencedirect.com/science/article/pii/ S2214157X24012413
Researchers are exploring innovative solutions for thermal energy storage to address the challenges posed by intermittent renewable sources, enhance energy efficiency, and contribute to the global shift towards cleaner and more sustainable energy practices. In the pursuit of an optimal system to improve the heat release/absorption efficiency of phase change materials (PCMs), a unique shell and tube latent heat storage system with four rectangular fins was designed. The melting and solidification behaviors of the material in this device were examined by manipulating the tube s position within the shell and fins around the tube. Six different cases were considered such as case A (tube in the center of the shell), case B (tube at the top of the shell), case C (tube at the bottom of the shell), case D (tube in the center of the shell with fins on its sides), case E (tube at the top of the shell with fins located in its bottom section), and case F (tube at the bottom of the shell with fins located in its top section). Cases D and E were the best options for absorbing and releasing heat in the shortest time. However, it should be noted that case F was faster during the melting process and dropped behind in the final stages. The authors proposed that if achieving a balanced result without incurring additional costs is essential, case D is a suitable option since it offers reasonable performance in melting and solidification processes. However, suppose researchers and developers of energy storage systems are seeking higher performances where heat absorption and release occur much more rapidly. In that case, it is suggested to construct one of the cases, E or F, and implement a rotational mechanism to enable access to the other case. Based on the outcomes, cases D and F needed 6235 s and 7552 s, respectively, to fully melt. While all cases even required more than 3 h to solidify 80 % of the PCM. The complete melting speed of Case F is 21.12 % faster than that of Case D. Additionally, the time required for 50 % solidification is 14.79 % faster for Case E compared to Case D. During 3 h, this system could absorb 1172 kJ of energy (cases D and F) and release 893 kJ of energy (cases D and E). https://www.sciencedirect.com/science/article/abs/pii/ S2352152X24014658?via%3Dihub
Abstract Entropy generation and convection heat transfer in a partially porous chamber with different side wall temperatures using CuO–H2O have been investigated. The importance of this issue is wide application of the results in solar collectors, thermal extrusion systems, heat exchangers, bio-medicine, nuclear waste disposal, etc. The innovation of the present work is related to the investigation of fluid and heat fields and entropy generation by using a matrix with subordination of porosity to the vertical axis and permeability, thermal conductivity, and viscositywith subordination of porosity. To obtain accurate results, the two-phase mixture model was used, and thermal conductivity and viscosity of nanofluid were simulated by experimental models by temperature and volume fraction dependence. Governing equations are solved by the FVM. The main findings indicate that the best and worst optimization factor will occur in the porous matrix ε = ε(y2) and ε = -ε(y2), respectively, which is 113 % and 86 % of NH of the homogeneous matrix, respectively. Also increasing the filling of the cavity, highly improves NH, so that the NH will reach from 1.19 to 1.82 with the increase of S from 0.25 to 1. https://www.sciencedirect.com/science/article/pii/ S2214157X24005161?via%3Dihub
The present work proposes an innovative system for decarbonizing the energy mix and accelerating the worldwide green transition process. The system is driven by a biomass digester integrated with the supercritical carbon dioxide cycle for power generation and a multi-effect desalination unit for drinkable water production. At the heart of this concept is additional hydrogen injection through a proton exchange membrane electrolyzer based on photovoltaic panels. The suggested innovative model s techno-environmental, sustainability, and economic aspects are assessed and compared with a similar system without hydrogen injection. Then, a comparative multi-criteria optimization is applied to find the most optimal conditions from various facets based on the genetic algorithm with machine learning techniques. Afterward, the system’s performance at different optimal conditions is analyzed and compared by evaluating the most significant techno-economic, environmental, and sustainability indicators. The parametric assessment comparing different models indicates that the proposed novel model, including increased hydrogen injection, surpasses the basic system in terms of performance efficiencies, emissions, and energy costs. In the first optimization scenario, the proposed method demonstrates robustness by achieving higher water production of 1456 kg/s, a lower total cost of 118 $/h, and a higher net power of 1.1 MW than the design condition. When considering the sustainability index, energy cost, and emission metric as the optimization objective, their values are altered from 0.81 to 0.85, 92.5 $/MWh to 89.7 $/MWh, and 64.2 kg/MWh to 53.6 kg/MWh. The results further show that when prioritizing the sustainability index, energy cost, and emission as objectives, all components perform better from the energy conversion quality aspect compared to the scenario where water production, total cost, and net power are the optimization objectives. Finally, it is observed that the combustion chamber and solar panels are the worst components from the irreversibility aspect because of the highest exergy destruction rate. https://www.sciencedirect.com/science/article/abs/pii/ S0957582024004786?via%3Dihub
Announcement of Participation Certificate in the European Energy Centre Course The European Energy Centre has announced the awarding of a Certificate of Participation to the Director of the Al-Mustaqbal Energy Research Center, Professor Salwan Obaid Waheed, in recognition of his successful completion of the seminar on "Renewable Energy Management Techniques" held in March 2025. This seminar represents a vital opportunity to discuss the latest trends and technologies in the field of renewable energy management, contributing to a better understanding of how to improve energy efficiency. Attendance at such events also supports the achievement of global goals related to sustainable energy, underscoring the Centre's commitment to fostering innovation and research in this critical field. The seminar brought together experts and specialists from various sectors, allowing for the exchange of ideas and experiences, and helping to build a strong network of professionals dedicated to enhancing sustainability in the energy industry.