Search Results (284)

This study explores the viability of ultra-high-performance concrete (UHPC) as a structural material for compressed air storage (CAES) systems, combining comprehensive experimental testing and numerical simulations. Scaled (1:20) CAES tanks were designed and tested experimentally under controlled pressure conditions up to 4 MPa (580 psi), employing strain gauges to measure strains in steel cylinders both with and without UHPC confinement. Finite element models (FEMs) developed using ANSYS Workbench 2024 simulated experimental conditions, enabling detailed analysis of strain distribution and structural behavior. Experimental and numerical results agreed closely, with hoop strain relative errors between 0.9% (UHPC-confined) and 1.9% (unconfined), confirming the numerical model’s accuracy. Additionally, the study investigated the role of a rubber interface layer integrated between the steel and UHPC, revealing its effectiveness in mitigating localized stress concentrations and enhancing strain distribution. Failure analyses conducted using the von Mises criterion for steel and the Drucker–Prager criterion for UHPC confirmed adequate safety factors, validating the structural integrity under anticipated operational pressures. Principal stresses from numerical analyses were scaled to real-world operational pressures. These thorough results highlight that incorporating rubber enhances the system’s structural performance. Full article

Solar-driven irrigation, a promising clean technology for agricultural water conservation, is constrained by mismatched photovoltaic (PV) pump outflow and irrigation demand, alongside unstable PV output. While compressed-air energy storage (CAES) shows mitigation potential, existing studies lack systematic explorations of pump water-lifting characteristics and supply capacity under coupled meteorological and air pressure effects, limiting its practical promotion. This study focuses on a solar-coupled compressed-air energy storage regulated sprinkler irrigation system (CAES-SPSI). Integrating experimental and theoretical methods, it establishes dynamic flow models for three DC diaphragm pumps considering combined PV output and outlet back pressure, introduces pressure loss and drop coefficients to construct a nozzle pressure dynamic model via calibration and iteration, and conducts a 1-hectare corn field case study. The results indicate the following: pump flow increases with PV power and decreases with outlet pressure (model deviation < 9.24%); nozzle pressure in pulse spraying shows logarithmic decline; CAES-SPSI operates 10 h/d, with hourly water-lifting capacity of 0.317–1.01 m³/h and daily cumulation of 6.71 m³; and the low-intensity and long-duration mode extends irrigation time, maintaining total volume and optimal soil moisture. This study innovatively incorporates dynamic air pressure potential energy into meteorological-PV coupling analysis, providing a universal method for quantifying pump flow changes, clarifying CAES-SPSI’s water–energy coupling mechanism, and offering a design basis for its agricultural application feasibility. Full article

The increasing share of renewable energy sources (RES) in modern power systems necessitates the development of efficient, large-scale energy storage technologies capable of mitigating generation variability. Liquid Air Energy Storage (LAES), particularly in its adiabatic form, has emerged as a promising candidate by leveraging thermal energy storage and high-pressure air liquefaction and regasification processes. Although LAES has been widely studied, the impact of ambient temperature on its performance remains insufficiently explored. This study addresses that gap by examining the thermodynamic response of an adiabatic LAES system under varying ambient air temperatures, ranging from 0 °C to 35 °C. A detailed mathematical model was developed and implemented in Aspen Hysys to simulate the system, incorporating dual refrigeration loops (methanol and propane), thermal oil intercooling, and multi-stage compression/expansion. Simulations were conducted for a reference charging power of 42.4 MW at 15 °C. The influence of external temperature was evaluated on key parameters including mass flow rate, unit energy consumption during liquefaction, energy recovery during expansion, and round-trip efficiency. Results indicate that ambient temperature has a marginal effect on overall LAES performance. Round-trip efficiency varied by only ±0.1% across the temperature spectrum, remaining around 58.3%. Mass flow rates and power output varied slightly, with changes in discharging power attributed to temperature-driven improvements in expansion process efficiency. These findings suggest that LAES installations can operate reliably across diverse climate zones with negligible performance loss, reinforcing their suitability for global deployment in grid-scale energy storage applications. Full article

Depending on the factors to which the soils are exposed, many properties and engineering parameters may change. In particular, the temperature parameter affects the strength of the soils, the degree of compressibility, permeability, void ratio, Atterberg limits, and many other parameters. In areas where high temperatures occur, such as heat piles and nuclear waste storage areas, alternative soil mixtures are needed that can stabilize or better optimize the behavior of the soils. For this purpose, additives with high heat transfer capacity and symmetry can be used. In this study, aluminum additive, which is known to have high conductivity, was used together with zeolite–bentonite mixtures. Aluminum-added mixtures were kept at different temperatures, and their thermal conductivity values were measured at the end of different periods. Measurements were first carried out at room temperature for all mixtures. Then, measurements were repeated at the end of 1, 3, and 10 days for 55 °C and 80 °C temperature values. At the end of the heating periods, the samples were left to cool to room temperature, and the thermal conductivity values were examined at the end of the heating–cooling cycle. Experimental results showed that thermal conductivity increased as temperature increased when the same period was taken as a basis, but an increase was observed for 1 and 3 day heating periods, while the thermal conductivity values for the 10th day decreased. The initial increase is attributed to the densification of the material due to the removal of free and weakly bound water or to the improvement of solid–solid contact paths. The subsequent decrease is due to microstructural deterioration, such as increased air-filled porosity, drying shrinkage, and microcracking due to thermal stresses, and material degradation caused by prolonged heating. In addition, thermal conductivity values of the mixtures under high temperature were estimated for days 100 and 365 using the DeepSeek method. The results showed that the thermal conductivity coefficients symmetrically decreased with increasing time. Full article

Compressed air energy storage in aquifers presents a promising approach for large-scale energy storage, yet its implementation is complicated by geochemical reactions, such as pyrite oxidation, which can impact reservoir integrity and operational efficiency. This study numerically investigates the evolution of reservoir characteristics and geochemical processes during CAESA operations to address these challenges. Using the TOUGHREACT simulator, we developed one-dimensional and two-dimensional reactive transport models based on the Pittsfield aquifer field test parameters to simulate coupled thermal-hydrological–chemical processes under varying injection rates, temperatures, reservoir depths, and operational cycles. The results demonstrate that higher injection rates induce greater near-well pressure buildup and extended thermal zones, while deeper reservoirs exhibit abrupt declines in pressure and gas saturation due to formation constraints. Geochemical analyses reveal that pyrite oxidation dominates, leading to oxygen depletion, groundwater acidification (pH reduction), and secondary mineral precipitation, such as goethite and hematite. These findings underscore the critical interplay between operational parameters and geochemical reactions, highlighting the need for optimized design to ensure long-term stability and efficiency of aquifer-based energy storage systems. Full article

Implementing compressed air energy storage (CAES) in lined caverns provides a promising technical solution for large-scale energy storage, and the reasonable selection of sealing materials is essential for its success. Flexible membrane materials including sprayable polymers, rubber sheets, and airbags have recently been considered economical and practical sealing options. However, research on flexible membrane sealed CAES caverns remains limited, particularly regarding their mechanical response and parameter sensitivities. To address this gap, an elastic multilayer thick-walled cylinder model verified by physical model tests is proposed. Analytical solutions for the stress and displacement fields of the surrounding rock and concrete lining are derived, and a calculation scheme is designed to evaluate the influence and sensitivity of key parameters. Results indicate that under high internal pressure, both the lining and surrounding rock undergo radial compression without yielding, whereas the lining experiences adverse tensile stresses in the hoop direction. The maximum hoop tensile stress reached the order of 1~3 MPa under typical CAES operating pressures, and tensile-compressive stress transformation may occur in the lining under certain parameter combinations. Sensitivity analysis further shows that internal pressure, in situ stress, surrounding rock elastic modulus, and cavern radius are the dominant factors influencing the mechanical behavior of the system, while geometric and lining parameters have secondary but non-negligible effects. The findings provide theoretical support for the stability analysis and material design of flexible membrane sealed CAES caverns and offer useful guidance for determining allowable operating pressures and selecting lining configurations. Full article

Artificial underground compressed air energy storage (CAES) caverns have the advantages of large capacity and flexible location. However, the location selection of CAES in conditions of hard shallowly buried rock requires comprehensive consideration of multi-field coupling effects and engineering constraints, and the decision-making process involves multiple criteria and strong uncertainty. Aimed at addressing the problems of the evaluation index system not being detailed enough and the weight determination being biased to a single subjective or objective method in the existing research, this paper constructs a multi-criteria site selection evaluation method for an artificial underground CAES chamber in hard shallowly buried rock. Firstly, starting from the four criteria layers of ground environment, construction convenience, regional geological characteristics, and basic geological characteristics, combined with literature research and expert investigation, an evaluation index system containing 13 indicators was established. Secondly, the analytic hierarchy process (AHP) and entropy weight method (EWM) were introduced, the combination of subjective weight and objective weight realized through game theory, and the comprehensive weight of each index obtained. Then, the VIKOR method was used to rank the four candidate sites—A, B, C, and D—and the results were compared with those of the weighted TOPSIS method and the weighted gray relational analysis method. The engineering example shows that site B has advantages in group utility value, individual regret value, and compromise index. It is judged the optimal scheme by the three methods, and the ranking is stable under different decision-making mechanism coefficients, which verifies the robustness and applicability of the AHP–EWM–VIKOR model. The results show that the proposed method can distinguish different site selection schemes more clearly, effectively and comprehensively reflect suitability under complex geological and engineering conditions, and provide quantitative decision support for engineering site selection of artificial underground CAES caverns. Full article

Compressed air energy storage in aquifers (CAESA) is a promising technology for large-scale, long-duration energy storage. The initial bubble, also known as cushion gas, is a prerequisite for system operation, as it creates the storage space and provides pressure support. However, the optimal amount of cushion gas needed to satisfy both energy storage scale and system cycle time (SCT) remains insufficiently studied. In this work, we investigate the relationship between cushion-gas masses and SCT under various energy storage scales using numerical simulations, and further analyze its impact on the maximum achievable energy storage scale through an orthogonal design encompassing nine geological conditions. Simulation results indicate that aquifer permeability, depth, and thickness impose a physical upper limit on achievable storage scales. Below this threshold, increasing cushion-gas mass approximately linearly enhances SCT, while beyond it, performance gains saturate. The effect of the air bubble on system performance is also influenced by well screen length. Sensitivity analysis suggests that larger injection masses are beneficial under high-permeability and deeper burial conditions, whereas excessive injection under unfavorable geological conditions can lead to inefficiency and wasted resources. Based on these findings, the recommended injection gas masses for different energy storage scales under the ideal model are provided, along with suggestions for gas injection configurations based on various geological conditions. This work provides a new approach for the design of initial bubble injection for a CAESA system. Full article

Underground gas storage (UGS), encompassing hydrogen, natural gas, and compressed air, is a cornerstone of large-scale energy transition strategies, offering seasonal balancing, security of supply, and integration with renewable energy systems. However, the complexity of geological conditions, multiphysics coupling, and operational uncertainties pose significant challenges for UGS design, monitoring, and optimization. Artificial intelligence (AI)—particularly machine learning and deep learning—has emerged as a powerful tool to overcome these challenges. This review systematically examines AI applications in underground storage types such as salt caverns, depleted hydrocarbon reservoirs, abandoned mines, and lined rock caverns using bibliometric and knowledge-graph analysis of 176 publications retrieved from the Web of Science Core Collection. The study revealed a rapid surge in AI-related research on UGS since 2017, with underground hydrogen storage emerging as the most dynamic and rapidly expanding research frontier. The results reveal six dominant research frontiers: (i) AI-assisted geological characterization and property prediction; (ii) physics-informed proxy modeling and multi-physics simulation; (iii) gas–rock–fluid interaction, wettability, and interfacial behavior prediction; (iv) injection-production process optimization; (v) intelligent design and construction of underground storage, especially salt caverns; and (vi) intelligent monitoring, optimization, and risk management. Despite these advances, challenges persist in data scarcity, physical consistency, and generalization. Future efforts should focus on hybrid physics-informed AI, digital twin-enabled operation, and multi-gas comparative frameworks to achieve safe, efficient, and intelligent underground storage systems aligned with global carbon neutrality. Full article

In response to grid peak-shaving requirements under renewable energy integration, this study investigates the thermodynamic performance of a 300 MW adiabatic compressed air energy storage (A-CAES) system, with a focus on optimizing electro-thermal efficiency through parametric analysis. A detailed thermodynamic model was developed to systematically evaluate the effects of compression/expansion stage configurations (2–4 stages), pressure ratios (4–6), and inter-stage outlet temperatures (120–190 °C) on system performance. The results demonstrate that variable-pressure operation improves round-trip efficiency by a 1.8% per unit compression ratio increase, while optimized inter-stage cooling (150 °C) reduces exergy destruction by 22.5%. Thermal efficiency monotonically improves with additional expansion stages, whereas electrical efficiency peaks at three stages (70%) before declining due to parasitic losses. Exergy analysis reveals that compressors and turbines account for 65% of total destruction, emphasizing the need for enhanced heat exchanger design. These findings provide actionable insights for balancing efficiency gains with operational constraints in large-scale A-CAES deployment. Full article

The paper examines the experimental performance of air–rock bed, oil only, and oil–rock bed systems for storing heat suitable for cooking applications. The air–rock bed system is charged using hot air from a compressed air tank, while the oil–rock bed system employs a resistive heating element to heat a small volume of oil, which then circulates naturally. The charging process for the oil systems was controlled by adjusting funnel heights, and temperature measurements were taken using thermocouples connected to a data logger. Both systems can store thermal energy ranging from 4.5 kWh to 8 kWh and achieve temperatures between 150 °C and 300 °C, depending on supply temperatures. The simpler oil–rock bed allows for the direct boiling of water using the high temperature produced, and tests indicated comparable boiling times between systems. The findings suggest that these heat storage systems could enhance the advancement and integration of solar cookers, enabling more flexible cooking options. Full article

Basic oxygen furnace slag (BOFS) is one of the major by-products of the steelmaking industry. Its limited utilization as a construction material is primarily attributed to its chemical properties, which hinder its stability and hydraulic activity due to its high free lime (f-CaO) content. This paper explores the performance of supplementary cementitious material (SCM) synthesized with ground granulated blast furnace slag (GGBFS), freshly produced BOFS (f-BOFS), and stockpiled BOFS (s-BOFS). A total of 10 mixtures with ordinary Portland cement (OPC) replacement percentages were assessed, maintaining a total replacement of 50% OPC, incorporating 15%, 25%, and 35% of each material by weight. The laboratory experimental program encompassed material characterization, fresh and hardened properties, pozzolanic activity, and durability assessment, with comparative studies conducted for each evaluation item. Test results indicate that f- or s-BOFS, when used with GGBFS, can be a viable alternative SCM with the potential for hydraulic activities and pozzolanic reaction. The newly synthesized SCMs demonstrated improved strength development in mortar mixtures. The mixture containing [15% f-BOFS + 35% GGBFS] achieved a 28-day compressive strength of 20.6 MPa, while the [25% BOFS + 25% GGBFS] blend reached a compressive strength of 19.7 MPa. These mixtures meet Grade 80 criteria as per ASTM C989/C989M Standard Specification for Slag Cement for Use in Concrete and Mortars. A performance-based ranking system was developed by integrating results from flowability, air content, strength activity index, drying shrinkage, alkali–silica reaction, and sulfate attack. The novelty of this work lies in assessing BOFS–GGBFS blends as SCMs using this multi-criteria approach to identify the most sustainable and technically viable mixtures. Moreover, the study highlights the influence of storage-induced weathering by directly comparing the reactivity and performance of f- and s-BOFSs in ternary blends, providing new insights into optimizing the utilization of slag. Notably, regardless of f- and s-BOFSs, proportions of [15% BOFS + 35% GGBFS] demonstrated superior strength development and achieved an excellent overall ranking. These findings confirm the potential of such slag blends as suitable SCMs for mortar and concrete applications, thereby advancing the sustainability and efficiency of cementitious materials. Full article

Flight safety is central to the certification process and relies on assessment methods that provide evidence acceptable to regulators. For drones operating as Advanced Air Mobility (AAM) platforms, this requires an accurate representation of the complex wind fields in urban areas. Large-eddy simulations (LES) of such environments generate datasets from hundreds of gigabytes to several terabytes, imposing heavy storage demands and limiting real-time use in simulation frameworks. To address this challenge, we apply a Convolutional Autoencoder (CAE) to compress a 40 m-deep section of an LES wind field. The dataset size was reduced from 7.5 GB to 651 MB, corresponding to a 91% compression ratio, while maintaining maximum magnitude errors within a few tenths of the spatio-temporal wind velocity. Predicted vehicle responses showed only marginal differences, with close agreement between the full LES and CAE reconstructions. These findings demonstrate that CAEs can significantly reduce the computational cost of urban wind field integration without compromising fidelity, thereby enabling the use of larger domains in real-time and supporting efficient sharing of disturbance models in collaborative studies. Full article

The accurate analysis of the sealing performance of underground lined cavern hydrogen storage is critical for enhancing the stability and economic viability of storage facilities. This study conducts an innovative investigation into hydrogen leakage behavior by developing a multiphysical coupled model for a composite system of support structures and surrounding rock in the operation process. By integrating Fick’s first law with the steady-state gas permeation equation, the gas leakage rates of stainless steel and polymer sealing layers are quantified, respectively. The Arrhenius equation is employed to characterize the effects of temperature on hydrogen permeability and the evolution of gas permeability. Thermalmechanical coupled effects across different materials within the storage system are further considered to accurately capture the hydrogen leakage process. The reliability of the established model is validated against analytical solutions and operational data from a real underground compressed air storage facility. The applicability of four materials—stainless steel, epoxy resin (EP), ethylene–vinyl alcohol copolymer (EVOH), and polyimide (PI)—as sealing layers in underground hydrogen storage caverns is evaluated, and the influences of four operational parameters (initial temperature, initial pressure, hydrogen injection temperature, and injection–production rate) on sealing layer performance are also systematically investigated. The results indicate that all four materials satisfy the required sealing performance standards, with stainless steel and EP demonstrating superior sealing performance. The initial temperature of the storage and the injection temperature of hydrogen significantly affect the circumferential stress in the sealing layer—a 10 K increase in initial temperature leads to an 11% rise in circumferential stress, while a 10 K increase in injection temperature results in a 10% increase. In addition, the initial storage pressure and the hydrogen injection rate exhibit a considerable influence on airtightness—a 1 MPa increase in initial pressure raises the leakage rate by 11%, and a 20 kg/s increase in injection rate leads to a 12% increase in leakage. This study provides a theoretical foundation for sealing material selection and parameter optimization in practical engineering applications of underground lined caverns for hydrogen storage. Full article

This research evaluates Battery Energy Storage Systems (BESS) and Compressed Air Vessels (CAV) as complementary solutions for enhancing micro-grid resilience, flexibility, and sustainability. BESS units ranging from 5 to 400 kWh were modeled using a Nonlinear Autoregressive Neural Network with Exogenous Inputs (NARX) neural network, achieving high SOC prediction accuracy with R² > 0.98 and MSE as low as 0.13 kWh². Larger batteries (400–800 kWh) effectively reduced grid purchases and redistributed surplus energy, improving system efficiency. CAVs were tested in pumped-storage mode, achieving 33.9–57.1% efficiency under 0.5–2 bar and high head conditions, offering long-duration, low-degradation storage. Waterhammer-induced CAV storage demonstrated reliable pressure capture when Reynolds number ≤ 75,000 and Volume Fraction Ratio, VFR > 11%, with a prototype reaching 6142 kW and 170 kWh at 50% air volume. CAVs proved modular, scalable, and environmentally robust, suitable for both energy and water management. Hybrid systems combining BESS and CAVs offer strategic advantages in balancing renewable intermittency. Machine learning and hydraulic modeling support intelligent control and adaptive dispatch. Together, these technologies enable future-ready micro-grids aligned with sustainability and grid stability goals. Full article