Abstract

The use of phase change materials (PCMs) in various applications, such as brick walls, cold thermal energy storage systems, solar water heating, and photovoltaic-thermal (PVT) systems suggests significant potential for improving energy efficiency and thermal performance. This review discusses key findings from recent studies on optimizing PCM use in different scenarios. Studies emphasize the importance of comprehensive evaluations to determine the economic viability and environmental impact of brick PCM systems. Further investigations are recommended to develop composite PCMs suitable for year-round use and improve thermal performance in brick wall applications. For cold thermal energy storage systems, enhancing heat storage density, solidification rates, and operational efficiency is essential for PCM-based systems to effectively replace fossil fuels. Meanwhile, incorporating tracking systems and nano-enhanced PCMs in solar water heating systems can lead to enhanced performance and sustainability. In PVT systems, refining the preparation of nanoparticle PCMs and integrating efficient thermal transfer fluids prove to be key areas for improvement. Additionally, the utilization of enhanced PCMs (EPCMs) in PV modules has demonstrated improved temperature control and increased photoelectric conversion efficiency. Findings highlight the potential of PCM integration in various systems and emphasize the need for further research to optimize performance, enhance efficiency, and address environmental considerations. Overall, this review provides key insights for professionals working in the field of PCM-based thermal energy storage.

Abstract

Battery modeling is a crucial method for battery design and management, in which understanding the variations in key electrochemical parameters is essential for lithium-ion battery(LIB) modeling. To analyze the sensitivity of electrochemical parameters under different conditions, this study investigates electrochemical parameters under different temperatures and states of charge (SOC) in a NCM vs. graphite lithium-ion battery. Half cells were designed and utilized to systematically acquire electrochemical parameters for the cathode. To delineate the correlation between working conditions (temperature, and SOC) and key electrochemical parameters, the evolution patterns of the electrochemical parameter matrix under different temperatures and SOC levels are quantified experimentally. Subsequently, through battery model simulation, the impacts of parameter adjustments on the external characteristics of the battery are assessed, revealing the mapping relationships among conditions, electrochemical parameters, and external battery attributes. The results indicate a high sensitivity of the solid-phase diffusion coefficient (D_s) to temperature. Within the temperature range of 10 °C to 55 °C, these parameters generally exhibit an increasing trend with rising temperature. Furthermore, as the temperature increases, both the peak values and their corresponding SOC values show a noticeable shift. Simulation results reveal that variations in these parameters significantly impact the impedance and voltage of the battery. This investigation furnishes insights for precise modeling and simulation.

Abstract

Achieving carbon neutrality and mitigating global warming demand effective management of anthropogenic CO2 emissions. Calcium Looping (CaL) technology, characterized by its reversible carbonization and calcination reactions, presents a cost-effective and environmentally benign approach for CO2 capture from coal-fired power plants. Integrating Concentrated Solar Heat (CSH) with CaL emerges as a promising avenue for cleaner production. Addressing calcium sintering issues in CaL projects, this study employed a wet precipitation method for Precipitated Calcium Carbonate (PCC) production to reactivate deactivated calcium material. Balancing the adsorbent flow rate and reactor temperature is paramount for cost-effective carbon dioxide capture. Consequently, this study conducted an optimization analysis of adsorbent flow rate and reactor parameters to enhance carbon capture efficiency and economic viability. Kinetics of the carbonator and calciner were modeled in Aspen. The findings revealed optimal temperatures of 600 °C for the carbonator and 900 °C for the calciner, considering system cost and CO2 capture efficiency. The study determined that the molar flux ratios of recycled absorber to CO2 in flue gas and supplemental absorber to CO2 in flue gas yielding the lowest cost are 0.09 and 4, respectively. The findings furnish valuable insights for configuring flow rates and temperatures in project construction, thereby fostering the commercialization of carbon capture initiatives, and advancing carbon neutrality goals.

Abstract

The carbonate reservoir is rich in oil and gas resources and has great exploration potential. The deep carbonate rock has poor drill-ability. Percussive drilling technology has been verified to notably enhance the rock-breaking efficiency in deep and ultra-deep formations. However, the law of rock breaking under different parameters of axial percussion is not clear. A set of experimental equipment for percussive drilling was established. A series of axial percussive drilling tests under different percussive parameters with a double-cutter PDC bit were carried out on carbonate rock. The study analyzed the relationship between axial force and percussive frequency, as well as the relationship between axial force and percussive amplitude. The results indicate that the average value and fluctuation amplitude of the axial force are minimized at the frequency of 16 Hz. When the amplitude of axial percussion reaches 0.8 mm, the average axial force is at its lowest point. The optimum axial percussive amplitude is 0.8 mm. Excessive percussive amplitude is detrimental to rock breaking. The findings will be helpful in determining the optimal parameters for axial percussive drilling.

Abstract

Ammonia is a perspective carbon-free fuel that can be used in gas turbines. In this paper, the concept of ammonia fed combined cycle power plant (CCPP) with chemically recuperated gas turbine (CRGT) is provided. The effect of operating parameters such as temperature, pressure and pressure ratio on the efficiency of combined cycle power plant is determined. The comparative analysis of CCPP with traditional gas turbine and chemically recuperated gas turbine is provided. The results of the thermodynamic analysis show that efficiency of CCPP with CRGT is significantly higher (up to 5.5%) than efficiency of CCPP with a conventional gas turbine without thermochemical exhaust heat recuperation. The ratio between the work output from steam turbine and gas turbine is determined to show that in CCPP with CRGT the higher amount of heat is used in gas turbine cycle. This ratio is 2-7% higher than for CCPP with simple gas turbine without exhaust heat recuperation.

Abstract

Fault diagnosis of partial discharge (PD) is crucial for the protection of overhead lines with covered conductors. Facing the challenge of identifying PDs that may have diverse fault patterns from background noise interferences, a novel intelligent fault diagnosis utilizing the large language model (LLM) is developed. To effectively apply LLM to PD diagnosis, the domain knowledge-based prompts are designed by incorporating the specific domain information, PD detection task description, and measurement data information. To further improve the capability of LLM reasoning antenna signals, a signal reprogramming method is adopted to align the modalities of the measured signals and natural language. Finally, an output projection is constructed to identify PD by taking in the features learned from the LLM, whose backbone model remains intact during the learning process. Experimental results validate the efficiency and effectiveness of the developed method.

Abstract

To provide continuous and trustworthy services to users, contemporary data centers rely significantly on a stable power supply. This power is generally obtained from main energy sources and is frequently supported by on-site uninterruptible power supply systems, which include battery storage and renewable energy solutions. However, a precise understanding of how various parameters within power and energy systems affect the reliability of the data center’s power supply remains largely unexplored. This research, therefore, evaluates the influence of several critical parameters in data center power and energy systems on reliability, utilizing two prevalent global sensitivity analysis techniques: the Morris method and the Sobol method. The findings reveal that the likelihood of power grid failure, the failure probability of diesel generator and the wind turbine failure rate are the primary determinants of reliability. Moreover, the Morris method proves to be more efficient than Sobol, reducing computation time by approximately 48.9% to 49.7%, while still delivering comparable outcomes in certain cases.

Abstract

For long-term waterflooding reservoirs with strong heterogeneousness, the high-intensity water flooding has caused significant time-variation in reservoir properties; Meanwhile, there are varying degrees of high-speed non-Darcy flow features in the thief zone. In this paper, a new physical-based dimensionality reduction numerical simulation method is proposed, which takes the one-dimensional connection flow units as the research object, with the dynamic transmissibility and pore volume as the characteristic parameters. By coupling the time-varying properties and high-speed non-Darcy flow, a fine simulation of strongly heterogeneous reservoirs considering the thief zone in long-term waterflooding has been realized. We analyze the effects of time-varying physical properties and high-speed non-Darcy flow on the dynamics of long-term waterflooding taking the channelized Egg model as an example. The results show that by the high-intensity water flooding, the physical properties and conductivity of the thief zone become better, which accelerates the waterflood front broken, the water cut rises, and the residual oil between the wells is enriched; On the opposite side of the small pore throat. Influenced by the non-Darcy flow in the thief zone, the conductivity and fluid seepage capacity in the thief zone decreases, and the inhibition of the water is more significant, and more water flows to the small pore throat, which enlarges the swept volume, and facilitates the recovery. Based on the method proposed in this article, a more precise and efficient simulation of heterogeneous waterflooding reservoirs has been achieved.

Abstract

Large-scale short-term aggregate load forecasting involves predicting energy consumption across geographic areas or large sets of users. This practice is crucial in power systems, particularly for energy suppliers. The widespread installation of smart metering technology has facilitated the collection of extensive data on user load profiles. By incorporating such granular data, large-scale load forecasting becomes more accurate and reliable, capturing the variability and trends across consumer segments. However, transforming smart-meter data into effective load forecasting models faces significant challenges. For instance, smart-meter data presents issues related to its high volume, variety, and fine-grained temporal resolution. Consequently, different techniques can be considered to mitigate these issues before applying forecasting models to the data. This paper conducts a two-step literature review to provide insight into the data, forecasting approaches, and model evaluation used in large-scale, short-term aggregate load forecasting from smart-meter data. We propose classifying the different forecasting approaches into integrated, residential-based, and cluster-based strategies. We additionally draw insight into the effect of data volume on forecasting models, performance comparison between forecasting approaches, and the tendency of model complexity in this research domain.

Abstract

In the comprehensive classification and evaluation of low permeability reservoirs with different geological genesis, the correlation between the classification results with gas permeability as the classification standard and the oil well productivity is low. In order to improve the correlation between the classification results of low permeability reservoirs with different geological genesis and the productivity of oil wells, the grey correlation degree (ri) between the evaluation parameters of low permeability reservoirs with different geological genesis and the reservoir seepage capacity (RQI) is calculated based on the grey correlation method. According to the size of ri, the main control evaluation parameters affecting RQI are obtained. Then the multivariate classification coefficient (Feci) of the main control evaluation parameters is calculated by the multivariate classification method, and the size of Feci is compared to comprehensively classify and evaluate the low permeability reservoirs with different geological genesis. Taking the three types of low permeability reservoirs with geological genesis in the S oilfield as an example, the main control evaluation parameters affecting the RQI of the three types of low permeability reservoirs with geological genesis are as follows : shale content, mainstream throat radius, crude oil viscosity, starting pressure gradient, movable fluid saturation and fracturing adaptability parameters. The Feci of the main controlling factors is calculated, and the reservoirs are classified into four categories. Feci>0.8 is class I reservoir, 0.4-0.8 is class II reservoir, 0.2-0.4 is class III reservoir, < 0.2 is class IV reservoir ; the R2 value of Feci and oil well productivity is 0.795, and the R2 value of gas permeability and oil well productivity is 0.0219.Grey correlation-multiple classification comprehensive classification results have better correlation with oil well productivity.