Search Results (292)

This study investigates long-term land surface temperature (LST) trends along the shoreline of Lake Drūkšiai, a transboundary lake in eastern Lithuania that formerly served as a cooling reservoir for the Ignalina Nuclear Power Plant (INPP). Although the INPP was decommissioned in 2009, its legacy continues to influence the lake’s thermal regime. Using Landsat 8 thermal infrared imagery and NDVI-based methods, we analysed spatial and temporal LST variations from 2013 to 2024. The results indicate persistent temperature anomalies and elevated LST values, particularly in zones previously affected by thermal discharges. The years 2020 and 2024 exhibited the highest average LST values; some years (e.g., 2018) showed lower readings due to localised environmental factors such as river inflow and seasonal variability. Despite a slight stabilisation observed in 2024, temperatures remain higher than those recorded in 2013, suggesting that pre-industrial thermal conditions have not yet been restored. These findings underscore the long-term environmental impacts of industrial activity and highlight the importance of satellite-based monitoring for the sustainable management of land, water resources, and coastal zones. Full article

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

To ensure the safety of nuclear power production, nuclear power plants deploy numerous sensors to monitor various physical indicators during production, enabling the early detection of anomalies. Efficient anomaly detection relies on complete sensor data. However, compared to conventional energy sources, the extreme physical environment of nuclear power plants is more likely to negatively impact the normal operation of sensors, compromising the integrity of the collected data. To address this issue, we propose an anomaly detection method for nuclear power data: Neural Normal Stochastic Process (NNSP). This method does not require imputing missing sensor data. Instead, it directly reads incomplete monitoring data through a sequentialization structure and encodes it as continuous latent representations in a neural network. This approach avoids additional “processing” of the raw data. Moreover, the continuity of these representations allows the decoder to specify supervisory signals at time points where data is missing or at future time points, thereby training the model to learn latent anomaly patterns in incomplete nuclear power monitoring data. Experimental results demonstrate that our model outperforms five mainstream baseline methods—ARMA, Isolation Forest, LSTM-AD, VAE, and NeutraL AD—in anomaly detection tasks on incomplete time series. On the Power Generation System (PGS) dataset with a 15% missing rate, our model achieves an F1 score of 83.72%, surpassing all baseline methods and maintaining strong performance across multiple industrial subsystems. Full article

This article is devoted to an overview of the conducted work and the current status of decommissioning of the world’s first BN-350 industrial fast neutron reactor. The reactor was put into operation on 16 July 1973 in Aktau. In 1999, the government of Kazakhstan decided to shut down the reactor, and from that moment to the present, it has been in the decommissioning stage. All work on decommissioning the reactor facility was grouped into five stages. The first stage was completed in 2010 when the spent fuel of the BN-350 reactor was placed for long-term storage. The second stage is nearing completion. Research is currently underway to develop technologies for processing radioactive sodium. The goal of the third and fourth stages of the BN-350 reactor decommissioning is the comprehensive processing of liquid and solid radioactive waste. Now such waste is stored in special storage directly on the territory of the nuclear power plant. Full article

Titanium has specific uses due to its cost, which is counterbalanced by its extraordinary chemical and physical properties. Submarine hulls and nuclear power plant pipes have been made of titanium since the last century due to its high corrosion resistance, and the aircraft industry has also exploited its remarkable properties, such as lightness and high melting point. Surface modifications by plasma electrolytic oxidation (PEO) may increase its corrosion resistance, roughness and wettability. Furthermore, greater corrosion resistance is a rather attractive property in nuclear power plant pipes, although the increased roughness and wettability are disadvantageous downsides as they favor the attachment of marine organisms. Nonetheless these new features are particularly interesting for biomedical applications. In this study, PEO films were produced on commercially pure titanium substrates using different electrolytes, one of which contains zirconium dioxide and the other consisting of tantalum pentoxide, in addition to a third one composed of a combination of the former two. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR) analyses were performed in addition to contact angle and roughness measurements, and electrochemical tests were carried out to comparatively characterize the different film compositions. The results revealed that excellent corrosion resistance was achieved by mixing oxides in the electrolyte. Full article

In this article, an R-vine copula model is proposed to detect the nonlinear interrelationships between the oil market and five Chinese new-energy-related stock markets from 2017 to 2022, i.e., photovoltaic, new energy vehicles, energy storage, wind power, and nuclear power industries. Firstly, the transmission of downward and upward risk spillover effects (RSEs) is measured from the oil market to the five Chinese new-energy-related stock markets. Subsequently, a CoVaR backtesting methodology is developed to demonstrate the availability of the R-vine copula-CoVaR model. The empirical studies strongly show that the oil market exhibits a significant asymmetric RSE on the five Chinese new-energy-related stock markets. Furthermore, different Chinese new-energy-related stock markets have varying responses to the positive and negative impacts of the oil market. Specifically, the photovoltaic, energy storage, and wind power industries are more sensitive to such adverse effects. However, the new energy vehicle and nuclear power industries are more likely to be positively affected. Full article

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article

The study explores the resistance of high-strength C40/50 concrete with steel fiber and silica fume admixture to high temperature and gamma radiation. The purpose is to create concrete composites with radiation shielding properties and high temperature resistance for use in nuclear power plants and radioactive waste storage facilities. For that purpose, concrete specimens containing 0.64 wt% industrial steel fiber and different proportions of silica fume (0%, 5%, 10%, 15%) were first subjected to high temperature according to ISO 834 and ASTM E119 after 28 days of curing at a target temperature of 900 °C based on a working fire scenario and then subjected to 94 kGy gamma radiation and analyzed using compressive strength, flexural strength, ultrasonic pulse velocity (UPV), SEM-EDX and XRD tests. It was found that 94 kGy gamma radiation increased the compressive strength of steel fiber concrete by SFC 20.98%, SFC-5 26.36%, SFC-10 26.45%, and SFC-15 25.34%, flexural strength by SFC 24.85%, SFC-5 25.06%, SFC-10 24.11%, and SFC-15 23.65%, and led to microstructure improvement and densification. XRD analysis revealed that samples exposed to 94 kGy gamma radiation accumulated and increased their calcite peak, resulting in decreased porosity and increased compressive and flexural strength. Under high temperature (900 °C) conditions, a significant decrease in the mechanical properties of concrete was observed in the compressive strength of SFC 78.99%, SFC-5 76.71%, SFC-10 76.62% and SFC-15 76.05% and in the flexural strength of SFC 79.44%, SFC-5 78.66%, SFC-10 79.68% and SFC-15 80.11%. In conclusion, results highlight the synergistic role of silica fume in reducing porosity and enhancing radiation-induced cement matrix reactivity, as well as that of steel fibers in improving thermal shock resistance and residual mechanical integrity. The developed composite materials are promising candidates for structural and shielding components in nuclear reactors, radioactive waste storage units, and other critical infrastructures requiring long-term durability under combined thermal and radiological loading. Full article

The design of a country’s nuclear energy development policy and legal system is crucial to the development of its nuclear energy industry, and thus also affects international issues such as climate change and energy green and low-carbon transformation. Under such a “Nuclear Relaunch” era that the world is experiencing, China’s nuclear power installed capacity has reached second in the world, and China’s nuclear energy policies and laws will have a significant impact on the development of civil nuclear energy worldwide. Therefore, it is crucial to reflect on the problems existing in China’s nuclear legal system and theoretical research and propose corresponding amendments based on the review of China’s existing nuclear energy policy and law and the comparison with the relevant system design of other countries. This paper first extracts the common clues of nuclear power development in the world through historical and comparative studies on the development of nuclear energy policies and laws in China and other countries in the world. Secondly, combined with relevant data such as the scale of China’s nuclear power industry, the number and focus of policies and laws, this paper comprehensively analyzes and points out the current practical difficulties faced by China’s nuclear energy policies and laws from an empirical perspective. Finally, in response to these practical difficulties, this paper will propose amendments such as promoting atomic energy legislation, improving the nuclear safety legal standard system and independent supervision system, and strengthening information disclosure in the field of nuclear energy. Full article

This study presents an innovative approach to the sustainable valorization of industrial sweet cherry (Prunus avium L.) waste from the Vignola Region, Italy, transforming what is typically discarded into a high-value bioactive resource. Unlike conventional extractions, our hydroethanolic extract (VCE) was obtained from the entire cherry waste, including the pericarp, pulp, and stone, as generated by industrial processing. This full-fruit extraction strategy represents a novel and efficient use of agricultural by-products, aligning with circular bioeconomy principles. Sweet cherries are known for their phenolic richness, and spectrophotometric assays (TPC, TFC, reducing power, DPPH, and ABTS) confirmed the extract’s antioxidant capacity. In vitro studies using RAW 264.7 macrophages revealed no cytotoxic effects (MTT assay), along with significant anti-inflammatory activity, evidenced by reduced ROS and NO production and downregulation of iNOS and COX-2. Western blotting showed inhibition of NF-κB nuclear translocation and MAPK pathway signaling. Additionally, agarose gel electrophoresis showed protection against oxidative DNA damage. UPLC-MS/MS analysis identified sakuranetin, aequinetin, and dihydrowogonin as the most representative compounds in VCE. Molecular docking simulations revealed strong and specific binding affinities of these compounds to NF-κB p65 and key MAPK targets. These findings highlight whole sweet cherry waste—including the pit—as a potent and sustainable source of bioactive compounds with promising nutraceutical and pharmaceutical applications. Full article

Decarbonization stands as one of humanity’s most pressing challenges, demanding collective efforts from multiple sectors to meet established goals. The transportation industry plays a pivotal role in this endeavor, with the maritime sector offering significant potential to reduce emissions. As a cornerstone of global goods and commodity transport, the maritime industry is uniquely positioned to contribute meaningfully to the global drive for lower carbon emissions. Artificial intelligence (AI), with its profound influence across diverse domains, is anticipated to play a vital role in supporting the nuclear shipping industry on its path to a decarbonized future. Specifically, AI provides tools to make nuclear power on ships a more economically viable solution while enhancing the safety and security of nuclear systems. This paper explores AI tools as an enabler for adopting nuclear-powered ships, delving into the challenges and opportunities associated with their implementation. Ultimately, it highlights AI’s role in fostering sustainable nuclear-powered maritime solutions, which align with and contribute to achieving global decarbonization goals. Full article

Recently, there has been a growing interest in issues related to mining equipment maintenance, with particular focus on pumping systems’ continuous operation. However, despite wide applications of pump system maintenance in a wide range of industries, such as water and wastewater, aviation, petrochemical, building (HVAC system), and nuclear power plant industries, the literature on maintenance of pump systems operating in the mining industry still needs development. This study aims to review the existing literature to present an up-to-date analysis of maintenance strategies for mining pumps, with a particular focus on proactive maintenance approaches. Key aspects considered include predictive diagnostics and prognosis, health status monitoring, maintenance management, and the integration of intelligent mining systems to enhance operational reliability and efficiency in harsh mining environments. The proposed methodology includes a systematic literature review with the use of the Primo multi-search tool, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The selection criteria focused on English studies published between 2005 and 2024, resulting in 88 highly relevant papers. These papers were categorized into six groups: (a) condition/health status monitoring, (b) dewatering system operation and maintenance, (c) health diagnosis and prognosis, (d) intelligent mining (modern technologies), (e) maintenance management, and (f) operational efficiency and reliability optimization. A notable strength of this study is its use of diverse scientific databases facilitated by the multi-search tool. Additionally, a bibliometric analysis was performed, showcasing the evolution of research on pump maintenance in the mining sector over the past decade and identifying key areas such as predictive diagnostics, dewatering system optimization, and intelligent maintenance management. This study highlights the varied levels of research and practical implementation across industries, emphasizing the mining sector’s unique challenges and opportunities. Significant research gaps were identified, including the need for tailored diagnostic tools, real-time monitoring systems, and cost-effective maintenance strategies specific to harsh mining environments. Future research directions are proposed, focusing on advancing predictive maintenance technologies, integrating intelligent systems, and enhancing operational efficiency and reliability. The study concludes with a detailed discussion of the findings and their implications, offering a roadmap for innovations in pump maintenance within the mining industry. Full article

This study investigates the sources and distribution of heavy metals in the Styr River, particularly in the area influenced by the cooling water blowdown from the Rivne Nuclear Power Plant (Ukraine). The concentrations of eight heavy metals (Zn, Cd, Pb, Cu, Ni, Mn, As, and Cr) were measured over a period from 2018 to 2022. Monthly water samples were collected and analyzed using an inductively coupled plasma optical emission spectroscopy (ICAP 7400 Duo, Thermo Fisher Scientific, Waltham, MA, USA). The results show that the average concentrations (M ± SD) of the heavy metals decreased in the following order: Cu (6.43 ± 1.82 ppb), As (5.1 ± 0.2 ppb), Zn (4.67 ± 1.14 ppb), Mn (4.03 ± 2.81 ppb), Ni (3.3 ± 0.8 ppb), Cr (1.06 ± 0.22 ppb), Pb (1.05 ± 0.11 ppb), and Cd (1.01 ± 0.03 ppb). Seasonal and annual variations in metal concentrations were observed, with notable decreases in Zn, Cu, and Mn in 2021, likely due to anthropogenic activities. Pearson correlation analysis and cluster analysis were employed to explore relationships between the metals. The findings suggest that certain metals, such as Pb, Cr, and Ni, share common sources, likely industrial emissions or urban pollution, while others, such as Cd and As, have more isolated sources. This research highlights the complex interplay of natural and anthropogenic factors influencing heavy metal levels in the Styr River. Full article

It is widely recognized that economies of scale enhance the competitiveness of large-scale nuclear reactors compared to light-water small modular reactors (SMRs). As such, choosing an appropriate strategy to enhance competitiveness is crucial for the future of SMRs. Their development is still in the early stages, and among the leading projects, two distinct approaches to technical innovation can be observed. In some projects, technical innovations are rejected because they are perceived as triggers for risky, costly, and long-term processes. In short, this means that the competitive advantage is based primarily on modular design and the benefits of long production runs, which might require at least a few successful implementations. Examples of this approach include the Westinghouse AP300 and Rolls-Royce SMR designs. In other projects, technical innovations are viewed as a means to achieve substantial cost reductions. Here, the initial challenge is to prove that the proposed solutions are safe. Next, it must be demonstrated that their implementation and operation meet the designers’ expectations. These goals can be achieved with the first implementation. Such an approach is exemplified, for instance, in the NuScale and GEH BWRX-300 projects. Currently, available economic analyses show that it is challenging not only to identify the most promising SMR projects but also to determine which approach to technical innovation will ultimately be more effective. Therefore, it is worth examining how leading SMR projects have improved their competitiveness. Additionally, it is important to remember that, even if light-water SMRs are not deployed, it is likely that some of their innovative solutions will be incorporated into other advanced nuclear power plant designs and potentially applied beyond the nuclear industry. Full article

Intelligent fault diagnosis (IFD) plays a crucial role in reducing maintenance costs and enhancing the reliability of safety-critical energy systems (SCESs). In recent years, deep learning-based IFD methods have achieved high fault diagnosis accuracy extracting implicit higher-order correlations between features. However, the excessive long training time of deep learning models conflicts with the requirements of real-time analysis for IFD, hindering their further application in practical industrial environments. To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). Specifically, the broad learning system (BLS), known for its low time complexity and high classification accuracy, is adopted as an alternative to deep learning for fault diagnosis in SCES. Furthermore, EBLS is designed to enhance model stability and classification accuracy with high-dimensional small samples by incorporating the random forest (RF) algorithm and an ensemble strategy into the traditional BLS framework. In order to reduce the computational cost of the EBLS, which is constrained by the selection of its hyperparameters, the PSO algorithm is employed to optimize the hyperparameters of the EBLS. Finally, the model is validated through simulated data from a complex nuclear power plant (NPP). Numerical experiments reveal that the proposed method significantly improved the diagnostic efficiency while maintaining high accuracy. In summary, the proposed approach shows great promise for boosting the capabilities of the IFD models for SCES. Full article