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Motivated by the suboptimal performances observed in existing compressed air energy storage (CAES) systems, this work focuses on the efficiency optimization of CAES through thermal energy storage (TES) integration. The research explores the dependence of CAES performance on power plant layout, charging time, discharging time, available power, and cavern volume. Hence, a range of solutions are examined, encompassing both solid and liquid TES options, alongside the potential utilization of external air heaters. Inefficiencies in solid TES due to significant retention of thermal power within the medium after complete discharge are identified and mitigated through optimization strategies. In addition, solutions to prevent ice formation at the low-pressure expander phase are suggested to avoid icing issues in CAES layouts with liquid TES. Through this comprehensive investigation, the study provides valuable insights into enhancing the efficiency and sustainability of CAES systems. By constructing a volume–power–time conversion table, the research contributes to the advancement of CAES technology, facilitating more efficient energy storage and utilization, thereby addressing critical challenges in the field of energy storage.

Energy storage systems are increasingly gaining importance with regard to their role in achieving load levelling, especially for matching intermittent sources of renewable energy with customer demand, as well as for storing excess nuclear or thermal power during the daily cycle. Compressed air energy storage (CAES), with its high reliability, economic feasibility, and low environmental impact, is a promising method for large-scale energy storage. Although there are only two large-scale CAES plants in existence, recently, a number of CAES projects have been initiated around the world, and some innovative concepts of CAES have been proposed. Existing CAES plants have some disadvantages such as energy loss due to dissipation of heat of compression, use of fossil fuels, and dependence on geological formations. This paper reviews the main drawbacks of the existing CAES systems and presents some innovative concepts of CAES, such as adiabatic CAES, isothermal CAES, micro-CAES combined with air-cycle heating and cooling, and constant-pressure CAES combined with pumped hydro storage that can address such problems and widen the scope of CAES applications, by energy and exergy analyses. These analyses greatly help us to understand the characteristics of each CAES system and compare different CAES systems.

This paper introduces, describes, and compares the energy storage technologies of Compressed Air Energy Storage (CAES) and Liquid Air Energy Storage (LAES). Given the significant transformation the power industry has witnessed in the past decade, a noticeable lack of novel energy storage technologies spanning various power levels has emerged. To bridge this gap, CAES and LAES emerge as promising alternatives for diverse applications. The paper offers a succinct overview and synthesis of these two energy storage methods, outlining their core operational principles, practical implementations, crucial parameters, and potential system configurations. The article also highlights approaches to enhance the efficiency of these technologies and underscores the roles of thermal energy storage within their processes. Furthermore, it delves into the discussion of the significance of hybrid systems and polygeneration in the contexts of CAES and LAES technologies. Moreover, we briefly explore the potential integration of these technologies into other power systems.

In this research, a recommendation system was designed for optimizing the injection molding process parameters. The system incorporates the utilization of process windows, eXtreme Gradient Boosting (XGBoost), and genetic algorithms. Computer-aided engineering (CAE) simulations were conducted to generate process window data and simulation data. Automatic hyperparameter optimization of the XGBoost was performed using grid search and cross-validation methods. The system employs 5 injection molding feature parameters as input and one product feature as output, and the strengthen elitist genetic algorithms (SEGA) was used for predicting the optimal injection molding process parameters. The performance of the prediction model was evaluated using an RMSE of 0.0202 and an R2 of 0.9826. The accuracy of the system was verified by conducting real production. The deviation of the product weight obtained from real production from the desired weight is 0.22%, which means that the prediction model achieves a correct rate of 99.78%. This recommendation system has a significant application value in reducing production costs and cycle time, as it can provide initial injection process parameter suggestions solely through the mold’s digital data.

As one of the most important applications of digitalization, intelligence, and service, the digital twin (DT) breaks through the constraints of time, space, cost, and security on physical entities, expands and optimizes the relevant functions of physical entities, and enhances their application value. This phenomenon has been widely studied in academia and industry. In this study, the concept and definition of DT, as utilized by scholars and researchers in various fields of industry, are summarized. The internal association between DT and related technologies is explained. The four stages of DT development history are identified. The fundamentals of the technology, evaluation indexes, and model frameworks are reviewed. Subsequently, a conceptual ternary model of DT based on time, space, and logic is proposed. The technology and application status of typical DT systems are described. Finally, the current technical challenges of DT technology are analyzed, and directions for future development are discussed.

The mechanism of liquefaction and the factors that cause liquefaction behavior have previously been examined and evaluated, both analytically and experimentally; construction including liquefaction countermeasures is being implemented, based on these findings. This study presents a theoretical visualization of the mechanism of liquefaction generation and evaluates the behavior of particles in the ground. Specifically, an MPSM-DEM coupled CAE system (CAES) is employed to view the events beneath the ground, modeled three-dimensionally when an external acceleration is applied to simulate seismic waves and reveals the behavior below the surface. The numerical simulation of the liquefaction phenomenon, as represented by an MPSM-DEM coupled CAES system, clearly showed the mechanism of liquefaction generation and contributed to the design and accountability of more economical and sustainable liquefaction countermeasures, regardless of the field of specialization.

The potential energy of compressed air represents a multi-application source of power. Historically employed to drive certain manufacturing or transportation systems, it became a source of vehicle propulsion in the late 19th century. During the second half of the 20th century, significant efforts were directed towards harnessing pressurized air for the storage of electrical energy. Today’s systems, which are based on storing the air at a high pressure, are usually recognized as compressed air energy storage (CAES) installations. This paper aims to provide an overview of different technologies that take advantage of the energy accumulated in the compressed air. Particular attention is paid to the CAES installations that are working as electrical energy storage systems (EESs). These systems, developed originally as large capacity (>100 MWe) and fuel-based installations, may soon become fully scalable, highly efficient, and fuel-free electrical energy storage systems. To present this opportunity, a thorough review encompassing previous and up-to-date advancements in their development was carried out. In particular, CAES concepts, such as diabatic (D-CAES), adiabatic (A-CAES), and isothermal (I-CAES), are described in detail. This review also provides the detailed characteristics of the crucial elements of these configurations, including compressors, expanders, air storage chambers, and thermal storage tanks. Knowledge of these components and their role allows us to understand the main challenges behind the further development of the mentioned CAES setups. Apart from the CAES systems that are designed as EES systems, this paper describes other prospective technologies that utilize the energy of pressurized air. Accordingly, compressed air cars and their key elements are explained in detail. Moreover, the technology renowned as wave-driven compressed air energy storage (W-CAES) is described as well, indicating that the utilization of pressurized air represents a viable option for converting ocean energy into electrical power.

The extra-ordinary properties of carbon fiber reinforced polymer (CFRP) composites and its outstanding performance in their structural applications, especially in aerospace industry where high strength to weight ratio is highly desirable, have given them a steadfast attention in the engineering materials research world. Drilling of CFRP materials to facilitate holes for component assembly subjects them to various modes of failure due to their anisotropy and in homogeneity. A requisite means of investigation and analysis is therefore required in order to understand these failure modes. Traditionally, the failure modes have been studied through experiments and more recently numerical simulations. There are dozens of finite element (FE) software available for numerical simulation with Abaqus/CAE being the mostly used for composites analysis. The numerical setup is a complicated process requiring some level of expertise in order to get accurate results which can be validated with experimental results. Upcoming researchers find it difficult, and they end up spending much time preparing for a successful simulation due to scattered information in scientific literature about modeling. This article tries to make work easier for new researchers by doing a review of 3D modeling of drilling CFRP using Abaqus/CAE software. The article features models from highly experienced researchers and their validated results with step-by-step setup guidelines. The objective is to provide a basic guideline to new researchers on the appropriate material properties and damage models for a successful and accurate simulation.

Model-based predictive control (MPC) describes a set of advanced control methods, which make use of a process model to predict the future behavior of the controlled system. By solving a—potentially constrained—optimization problem, MPC determines the control law implicitly. This shifts the effort for the design of a controller towards modeling of the to-be-controlled process. Since such models are available in many fields of engineering, the initial hurdle for applying control is deceased with MPC. Its implicit formulation maintains the physical understanding of the system parameters facilitating the tuning of the controller. Model-based predictive control (MPC) can even control systems, which cannot be controlled by conventional feedback controllers. With most of the theory laid out, it is time for a concise summary of it and an application-driven survey. This review article should serve as such. While in the beginnings of MPC, several widely noticed review paper have been published, a comprehensive overview on the latest developments, and on applications, is missing today. This article reviews the current state of the art including theory, historic evolution, and practical considerations to create intuitive understanding. We lay special attention on applications in order to demonstrate what is already possible today. Furthermore, we provide detailed discussion on implantation details in general and strategies to cope with the computational burden—still a major factor in the design of MPC. Besides key methods in the development of MPC, this review points to the future trends emphasizing why they are the next logical steps in MPC.

The concepts of digital twins (DTs) have been widely studied to predict system performance, shorten design cycles, and implement preventive maintenance, but mainly, in large-scale enterprises. It is extremely beneficial to the whole manufacturing sector, since DTs can be readily implemented in small and medium-sized enterprises (SMEs) with basic computer aided engineering (CAE) tools; over 95% enterprises are SMEs. This paper aims to prove the feasibility of using commercial CAE tools, such as SolidWorks Simulation, to design air-quenching processes for SMEs. SMEs can benefit to explore new business opportunities, reduce system design cycle, and improve existing air-quenching processes. To our knowledge, it will be the first work of adopting DTs in conceptual design of an air-quenching process in sense that (1) the need of simulating an air-quenching process before physical implementation is discussed thoroughly; (2) heat transfer processes are classified, governing mathematical models for various heat transfer behaviors are introduced to present an evaluation model of a heat transfer process; (3) main process variables of air-quenching are identified; (4) a DT of an air-quenching process is developed and simulated to verify the capabilities of commercial SolidWorks Simulation; (5) case studies are developed to show how a CAE tool can be used in DTs. The findings from the reported work are summarized with a debrief of our future work.