Normal Operating Conditions Research Articles

Simulation Results of a Thermal Power Dispatch System from a Generic Pressurized Water Reactor in Normal and Abnormal Operating Conditions

Simulation Results of a Thermal Power Dispatch System from a Generic Pressurized Water Reactor in Normal and Abnormal Operating Conditions

Assessment of whole-site methane emissions from anaerobic digestion plants: Towards establishing emission factors for various plant configurations

This study examines methane (CH4) emission factors from biogas and wastewater treatment plants, based on primary and secondary data collected from 109 facilities. Primary emission data were measured at 19 facilities representing prevalent plant configurations across Europe. Statistical analysis highlights two categorical variables, namely primary feedstock and plant size, expressed as CH4 production (≤250 kgh−1: small and medium-sized plants, >250 kgh−1: large plants), each of which has a significant impact on whole-site CH4 emissions. Additionally, digestate storage (gastight vs. not-gastight) has a meaningful effect when considering CH4 production as a continuous variable in the statistical analysis.Our results indicate that wastewater treatment plants have the highest average CH4 losses (7.0 % of CH4 produced, n = 31 or 0.10 kgpopulation equivalent(PE)-1yr−1, n = 28), followed by manure-based plants (3.7 %, n = 49), biowaste treatment facilities (2.8 %, n = 11) and energy crop-processing plants (1.9 %, n = 14). Furthermore, small and medium-sized plants have elevated emissions (5.6 %, n = 67) compared to larger counterparts (2.2 %, n = 42), primarily attributed to the absence of gastight digestate storage. Emissions tend to be lower with gastight digestate storage (2.7 %, n = 61) than not-gastight storage options (6.2 %, n = 48).Emission factors were determined for normal operating conditions, with a further investigation into other-than-normal operating conditions revealing temporal or constant emission peaks in eight out of 19 facilities. These peaks, suggesting potential areas for targeted mitigation strategies, were attributed to pressure relief valves, flare ignition problems and major leakages.

Nonlinear system dynamics simulation of thermal processes in the VVER-1200 reactor core

Abstract This study presents developing and validating a nonlinear dynamic model for simulating thermal processes in the VVER-1200 reactor core, a third-generation pressurized water reactor with enhanced safety features and improved thermal efficiency. The model employs a point kinetic mathematical approach, incorporating key neutronic parameters extracted from previous analyses of the VVER-1200 under normal operating conditions. These parameters, including neutron flux distribution, power peaking factors, and reactivity coefficients, were integrated into the reactor kinetic model to capture complex feedback mechanisms. In this study, the variation of reactor power with control rod insertion is examined under normal operating conditions. The analysis focuses on how different levels of control rod insertion influence reactor performance, providing insights into optimizing operational efficiency and safety. Monte Carlo code MCNP6 was used to estimate neutronic and kinetic parameters for the proposed model. The model’s validation involved comparing simulation results against detailed Monte Carlo N-Particle calculations, specifically focusing on power changes due to reactivity variations induced by control rod movements. Results showed strong agreement between the nonlinear dynamic model and MCNP calculations. The validated model provides valuable insights into the VVER-1200 core’s thermal behavior under steady-state and transient conditions. It can investigate critical hot spots, identify thermal limits, and assess system perturbation responses. The model’s computational efficiency makes it a practical tool for further optimization and safety analyses of the VVER-1200 design.

Application of neural networks for predicting electric load

The paper shows that the operational management of the power consumption regime is reduced to solving the problem of operational forecasting of the enterprise's load. The paper analyzes the works devoted to the forecasting of electric loads of power systems and industrial enterprises. It is shown that in order to achieve the required forecast accuracy, it is advisable to use adaptive forecasting procedures and, in particular, to use artificial neural networks. The use of artificial neural networks for forecasting the load of industrial enterprises is due to their properties, such as the ability to learn, reliability with incomplete input information, and the rapid response of the learned network to input influences. The conditions for determining the configuration of a neural network are considered. The structure of a neural network for predicting the electrical load of an industrial enterprise is presented. The process of training an artificial neural network with fitting the model to data from a retrospective sample is presented. The considered models of daily load forecasting were investigated on retrospective data on the modes of electricity consumption of a chemical enterprise with normalization of input data. The graphs of the actual total load and the forecast obtained by the artificial neural network models are presented. The research has shown that the use of an artificial neural network allows for a qualitative forecast of the enterprise's load under normal operating conditions of the equipment.

Hybrid Edge–Cloud Models for Bearing Failure Detection in a Fleet of Machines

Real-time condition monitoring of machinery is increasingly being adopted to minimize costs and enhance operational efficiency. By leveraging large-scale data acquisition and intelligent algorithms, failures can be detected and predicted, thereby reducing machine downtime. In this paper, we present a novel hybrid edge–cloud system for detecting rotational bearing failures using accelerometer data. We evaluate both supervised and unsupervised neural network approaches, highlighting their respective strengths and limitations. Supervised models demonstrate high accuracy but require labeled datasets representative of the failures of interesting data that are challenging to acquire due to the rarity of anomalies. Conversely, unsupervised models rely on data from normal operational conditions, which is more readily available. However, these models classify all deviations from normalcy as anomalies, including those unrelated to failure, leading to costly false positives. To address these challenges, we propose a distributed system that integrates supervised and unsupervised learning. A compact unsupervised model is deployed on edge devices near the machines to compress sensor data, which are then transmitted to a centralized cloud-based system. Over time, these data are automatically labeled and used to train a supervised model, improving the accuracy of failure predictions. Our approach enables efficient, scalable failure detection across a fleet of machines while balancing the trade-offs between supervised and unsupervised learning.

Development of a magnetic activator to protect an electric water heater against scale formation

The paper is dedicated to the research and development of a magnetic activator for the prevention of scale formation in electric water heaters, which is a pressing problem in regions with hard water, typical for Сentral Kazakhstan. During the operation of water heaters, salt deposits on heating elements lead to increased energy consumption and reduced efficiency. The developed activator uses powerful neodymium magnets that change the structure of the hardness salts in the water, preventing their deposition in the form of scale. Research has shown that the use of a magnetic activator can reduce water hardness by 15–20 %, resulting in a significant reduction in scale formation. In particular, under normal operating conditions, the thickness of the scale layer can reach 2–4 mm, but with the use of the magnetic activator this indicator is reduced to less than 1 mm. Descaling the heating elements not only extends the life of the water heater, but also reduces energy costs. Energy consumption is reduced by 10–15 %, as water heaters with less scale work more efficiently without requiring additional energy for heating. The paper also discusses the design features of the magnetic activator, the principles of its operation and the results of laboratory and field tests. Economic analysis has shown that the installation of the magnetic activator pays for itself within 1–2 years due to the reduction in limescale and energy costs. This makes it an attractive solution for private households as well as for industrial companies that use water heaters in their production process, as the proposed magnetic activator is an effective solution for protecting water heaters from the negative effects of hard water

Assessment of the impact of typhoons on crack propagation and fatigue life of OWT tower

Accurately predicting the effects of extreme weather on offshore wind turbines (OWT) is crucial for ensuring their safety and normal operation. This study combines fluid-structure interaction (FSI) and linear elastic fracture mechanics (LEFM) to simulate the crack propagation behavior of a 2.1 MW OWT tower in the offshore environment. First, ANSYS finite element software is used to simulate the OWT under normal operating conditions to determine the hot spot stress locations and introduce initial cracks at these locations. Then, the crack propagation process of the tower is simulated using FRANC3D software, and the fatigue crack propagation life (FCPL) is assessed using the Forman-Newman-de Koning (FNK) model. The study concludes that the middle and upper parts of the OWT tower are the most dangerous locations, influenced by aerodynamic and inertial loads, with initial cracks tending to extend linearly to both sides after penetrating the tower wall thickness. The occurrence of typhoons significantly reduces the FCPL and critical crack length of the tower, with the FCPL numerically decreasing from 4.75 years to 2.46 years, and the critical crack length decreasing from 1079.67 mm to 608.49 mm. The results of this study can provide a reference for maintenance and reinforcement work by technical maintenance personnel.

Research on structural vertical stiffness evaluation of track-specific suspension bridge under the action of the no-load first train

The track-specific bridge has the characteristics of a large volume of passenger traffic, narrow transverse width, and large excitation. The long-term and high-frequency operating conditions put forward strict requirements for the safety and comfort of the bridge. Aiming at the problem of achieving fast, accurate, and intelligent evaluation of bridges under the coupling action of trains and temperatures, this paper proposes a method for evaluating the structural vertical stiffness of track-specific suspension bridges under the action of the no-load first train. Firstly, the [Formula: see text] (deformation-velocity-time) data of trains on the bridge are analyzed through linkage numerical simulation, track train transponder, and structural safety monitoring system. Secondly, the CART algorithm establishes a regression model for data correction. The mapping relationship between the modified deformation monitoring value and the theoretical calculation value is analyzed to realize the current structural vertical stiffness evaluation. Finally, it is verified by the world’s largest span self-anchored track-specific suspension bridge. The results show that the theoretical deformation value of the most unfavorable deformation section is 268.55 mm, the actual monitoring value is 232.80 mm, the difference is 35.75 mm, and the relative error is 13.3%. The R2 score of the CART regression model is 0.94, and the accuracy of the CART regression model is higher than that of other algorithm regression models. The modified dynamic check coefficient is less than 1, and the ratio of the modified residual deformation value to the maximum deformation value is 7.6%, which meets the specification requirement not exceeding 20%, indicating that the current structural vertical stiffness is in normal operating conditions.

ANALYSIS OF THE RESULTS OF THE IMPLEMENTATION OF THE BAN ON THE SIMULTANEOUS INTRODUCTION OF POSITIVE REACTIVITY IN TWO OR MORE WAYS

The main reason for the prohibition of simultaneous injection of positive reactivity in two or more ways is the requirements of the nuclear safety rules for reactor installations. The introduction of positive reactivity by the means of influence on reactivity, provided for by the technical design of the reactor installation, should be excluded, if the emergency protection working bodies are not brought to the working position. Technical measures should exclude the possibility of introducing positive reactivity simultaneously by two or more provided means of influencing reactivity, as well as introducing positive reactivity by means of influencing reactivity during fuel loading and unloading. The rate of increase in reactivity by means of influencing the reactivity should not exceed 0.07 βef/s. For the working bodies of the control and protection system with an efficiency of more than 0.7 βef, the introduction of positive reactivity should be stepwise, with a step weight of no more than 0.3 βef (provided by technical measures). In the technical design of the RU, the size of the step, the pause between steps and the rate of increase in reactivity should be indicated. The consequence of introducing positive reactivity in two or more ways is a possible violation of the conditions for normal operation of nuclear power plants due to an uncontrolled increase in the neutron flux in operating and emergency modes of the reactor. The installed second-type warning protection, which is supposed to solve this problem, has shortcomings in its operation that lead to a clear violation of the current prohibition on the simultaneous introduction of positive reactivity in two or more ways. On the basis of the conducted research, the existing error in the operation of the PP-2 was identified and analyzed. To avoid the problem with the simultaneous introduction of positive reactivity in two or more ways, it is proposed to modernize the system of PP-2 with its further operation on reactivity indicators.

Contributon-Informed Approach to RPV Irradiation Study Using Hybrid Shielding Methodology

An important aspect of pressurized water reactor (PWR) lifetime monitoring is supporting radiation shielding analyses which can quantify various in-core and out-core effects induced in reactor materials by varying neutron–gamma fields. A good understanding of such a radiation environment during normal and accidental operating conditions is required by plant regulators to ensure proper shielding of equipment and working personnel. The complex design of a typical PWR is posing a deep penetration shielding problem for which an elaborate simulation model is needed, not only in geometrical aspects but also in efficient computational algorithms for solving particle transport. This paper presents such a hybrid shielding approach of FW-CADIS for characterization of the reactor pressure vessel (RPV) irradiation using SCALE6.2.4 code package. A fairly detailed Monte Carlo model (MC) of typical reactor internals was developed to capture all important streaming paths of fast neutrons which will backscatter the biological shield and thus enhance RPV irradiation through the cavity region. Several spatial differencing and angular segmentation options of the discrete ordinates SN flux solution were compared in connection to a SN mesh size and were inspected by VisIt code. To optimize MC neutron transport toward the upper RPV head, which is a particularly problematic region for particle transport, a deterministic solution of discrete ordinates in forward/adjoint mode was convoluted in a so-called contributon flux, which proved to be useful for subsequent SN mesh refinement and variance reduction (VR) parameters preparation. The pseudo-particle flux of contributons comes from spatial channel theory which can locate spatial regions important for contributing to a shielding response.

Data augmentation in predictive maintenance applicable to hydrogen combustion engines: a review

Machine-learning-based predictive maintenance models, i.e. models that predict breakdowns of machines based on condition information, have a high potential to minimize maintenance costs in industrial applications by determining the best possible time to perform maintenance. Modern machines have sensors that can collect all relevant data of the operating condition and for legacy machines which are still widely used in the industry, retrofit sensors are readily, easily and inexpensively available. With the help of this data it is possible to train such a predictive maintenance model. The main problem is that most data is obtained from normal operating conditions, whereas only limited data are from failures. This leads to highly unbalanced data sets, which makes it very difficult, if not impossible, to train a predictive maintenance model that can detect faults reliably and timely. Another issue is the lack of available real data due to privacy concerns. To address these problems, a suitable data generation strategy is needed. In this work, a literature review is conducted to identify a solution approach for a suitable data augmentation strategy that can be applied to our specific use case of hydrogen combustion engines in the automotive field. This literature review shows that, among the different state-of-the-art proposals, the most promising for the generation of reliable synthetic data are the ones based on generative models. The analysis of the different metrics used in the state of the art allows to identify the most suitable ones to evaluate the quality of generated signals. Finally, an open problem in research in this area is identified and it is the need to validate the plausibility of the data generated. The generation of results in this area will contribute decisively to the development of predictive maintenance models.

Reliability Assessment for Dual Constant‐Stress Accelerated Life Test Based on Weibull Distribution With Type‐II Censoring

ABSTRACTAccelerated life test (ALT) is a generally feasible and effective approach for evaluating the reliability of highly reliable products at normal operating conditions. This paper considers the reliability assessment for a dual constant‐stress ALT with Type‐II censoring for the Weibull distribution with constant shape parameter and a log‐linear link between the scale parameter and the stress factors. We derive the maximum likelihood estimates, weighted least squares estimates using a certain random variable transformation, and a combined maximum likelihood‐least squares estimates for the unknown parameters. In addition, we construct confidence intervals for the parameters of interest using both asymptotic theory and parametric bootstrap technique. Finally, simulation studies and an illustrative example are presented to demonstrate the effectiveness and feasibility of the proposed methods.

Coordinated Optimization Method for Distributed Energy Storage and Dynamic Reconfiguration to Enhance the Economy and Reliability of Distribution Network

To fully leverage the application potential of distributed energy storage systems (DESS) and network reconfiguration, a coordinated optimization method is proposed to enhance the economic efficiency of distribution networks under normal conditions and the reliability of a power supply during fault conditions. First, a scenario-generation method is developed based on Latin hypercube sampling and Kantorovich distance synchronous back-substitution reduction is used to obtain the typical scenario of wind and solar output. Next, a planning operation coordinated optimization framework and model are established, considering both normal and fault states of the distribution network. In the planning layer, the objective is to minimize the annual comprehensive capital expenditures for the distribution network to improve the economic efficiency of the distribution network. The operation layer includes both normal operation and fault operation states, with the optimization goal of minimizing the sum of normal operation costs and the fault costs associated with load shedding. Subsequently, a hybrid optimization algorithm combining an improved Aquila Optimizer-Second-Order Cone Programming (IAO-SOCP) is proposed to solve the coordinated optimization model. Finally, the proposed coordinated optimization method is validated using an enhanced IEEE 33-bus distribution network case study. The results demonstrate that the method effectively reduces network losses and minimizes load shedding costs during fault conditions, thereby ensuring a balance between the economic efficiency and reliability of the distribution network.

Optimization of emission strategies for airborne radioactive material from nuclear facilities under normal operating conditions

Optimization of emission strategies for airborne radioactive material from nuclear facilities under normal operating conditions

Hybrid data driven approach based on ANNs-PCA for wastewater treatment plant performance assessment

In this paper, a data driven method to assess and predict performance of full scale urban activated sludge wastewater treatment plant (WWTP) is presented. The proposed hybrid approach consists of a combination of artificial neural networks (ANNs) and principal component analysis (PCA). Measurement results of a municipal activated sludge WWTP operation of 1.3 million inhabitant equivalents are presented and discussed. In ANNs PCA design, the ANNs used to calculate a nonlinear and dynamic model of the processes under normal operating conditions. Besides, PCA is used to generate monitoring charts based on all measured parameters. Results highlight that ANNs-PCA monitoring is crucial tool that can be used to optimize and predict process spatiotemporal evaluation. This research results provide a practical strategy for improving operation, management and performance prediction of studied WWTP. This supports Sustainable Development Goal (SDG) 6: Clean Water and Sanitation and worldwide sustainability actions and efforts.

Power regulation of a wind farm through flexible operation of turbines using predictive control

Wind farms are becoming larger, presenting opportunities to improve their efficiency and effectiveness. Although wind farm control can be used to adjust a wind farm’s power output to meet grid requirements, its development is restricted by the limited flexibility of wind turbine operation. Wind turbines are typically designed to operate at the power output determined by the wind speed, making it difficult to adjust their power output quickly and easily in response to changes in grid demand. A new approach to wind farm control that provides full flexibility for both wind farms and turbines is proposed. This method adjusts the wind farm’s power production using predictive control of each turbine to meet the grid demands. The power generated by each turbine is adjusted to achieve this, keeping fluctuation in the wind farm power output low. A wind farm simulation is conducted under varying wind speeds using the discretized C MEX Matlab/SIMULINKR○ model of the 5 MW Supergen turbine and the wind turbine model from NREL. The results demonstrate that the wind farm power output tracks a reference value that can be set by the grid. The results are also analyzed on the torque/speed plane, and they indicate that the turbines operate within their specified safe operating zones under both normal and gusty wind operating conditions at the same time.

Engineering Management of Energy Utilities for Sustainable Development

Abstract The study proposes a model of engineering management of energy enterprises for sustainable development, which includes monitoring and evaluation of the technical condition of energy facilities, planning maintenance work based on the assessment results, and forecasting violations of normal operating conditions. Based on these data, the life cycle of energy equipment is managed, and the effectiveness of engineering decision-making is determined within the engineering management framework. The engineering management model is based on an integrated approach covering all equipment life cycle stages. On the example of pipeline systems of nuclear power plants, the article proposes to manage degradation processes and the sustainability of operations by reducing vibration load, which significantly impacts the efficiency, reliability, and safety of all power plant equipment. A damping vibration isolating support with an elastic rubber element is proposed, the advantage of which is the ability to adjust the stiffness of the support. Studies with the adjustment of the rigidity of the fasteners and the study of the amplitude-frequency characteristics of the pipeline section with additional damping supports showed a decrease in the maximum amplitudes of oscillations from 800 microns to 35 microns in section 1 and from 425 microns to 20 microns in section 2 and the effectiveness of their use for frequency restoration and reduction of oscillations.

Experimental and Numerical Analysis of Thermal Fatigue of Grey Cast Iron Ingot Mould.

This article presents the results of experimental studies and numerical calculations that were conducted to analyse the phenomena that occur during the operation of an ingot mould that is designed for casting steel ingots. The studies were conducted on an experimental stand in a foundry on an ingot mould that was designed to make ingots that weigh up to six tons; they consisted of determining the temperature of the ingot mould and measuring the displacements of its walls during filling with steel and cooling. These studies were used to create and verify a numerical model that was used to determine the temperatures, displacements, deformations, and stresses in ingot mould walls during the operating cycle using the FEM method. Microstructure studies of ingot cast iron that was subjected to thermal fatigue were also conducted on a laboratory stand; the temperature changes and test times were the same as those used under the normal operating conditions of the ingot mould. Cast iron samples were subjected to heating and cooling cycles within a range of 0 to 60 cycles; then, tensile tests were performed to determine their stress-strain curves. As a result of the conducted tests, a great influence was found of the number of cycles on decreases in the values of the modulus of elasticity and tensile strength-especially within a range of 0 to 10 cycles. A relationship was also found between the changes in these values and the image of the cast iron microstructure. Based on images of the cast iron microstructure after being subjected to different numbers of thermal fatigue cycles, the mechanism of the crack initiation and propagation was determined. The influence of the changes in the strength of the cast iron and the stress state that was determined by the FEM method on the durability of the tested type of ingot mould was analysed. The obtained research results will be useful for introducing design changes that are aimed at increasing the fatigue durability of ingot moulds.

Partially Reduced Tin Oxide As a Selective Electrochemical Ozone Production Catalyst

Rapid global urbanization has created a pressing need for clean and accessible water supplies. Traditional water treatment methods are often limited by the increasing demands and complexities associated with human population growth.1–3A critical factor widening the gap between industrial progress and environmental sustainability is the inability of current technologies to effectively address the growing issue of water contamination.4,5 Advancements in electrochemical water treatment methods could provide a promising path towards bridging this gap and offer sustainable solutions to global water scarcity.Among water treatment methods, the 6-electron electrochemical ozone production (EOP) reaction stands out. EOP offers a significant advantage because it allows for the on-site generation of ozone (O3), a powerful oxidizer with a minimal environmental footprint compared to other disinfectants.6 Nickel and antimony doped tin oxide (Ni/Sb-SnO2) is one of the most selective catalysts for EOP under normal operating conditions.7–10 Our previous work suggested that the primary function of antimony (Sb) in this catalyst is to enhance the electrical conductivity, which is crucial for optimal electrochemical performance. Conversely, the catalytic activity for EOP has been attributed to the presence of nickel (Ni) dopants. 9,11–13 Interestingly, the presence of n-type dopants, such as Sb, can cause catalyst instability.12,14 Herein, we explore a promising alternative approach to n-type doping: partial reduction of the catalyst. By partially reducing tin oxide, we can potentially achieve the desired conductivity enhancement without the drawbacks associated with destabilizing the catalyst. . In this work, through a combination of electroanalysis and spectroscopic detection of ozone, we confirm the selectivity of partially reduced tin oxides towards EOP. Furthermore, we employ various methods to rigorously assess the stability of this novel catalyst. Our findings establish a framework for the development of improved EOP catalysts with superior performance. This could potentially pave the way for significant advancements in the field of electrochemical water treatment.

Optimization design of centrifugal impeller based on Bezier surface and FFD space grid parameterization.

To enhance the aerodynamic performance of centrifugal impellers, this study presents an advanced optimization design methodology. This methodology addresses the challenges associated with numerous design variables, inflexible configurations, and low optimization efficiency. We propose two distinct spline function parameterization techniques: a global mapping model for Bezier surfaces and a local mapping model for Free-Form Deformation (FFD) control bodies. We investigate the impact of these parameterization methods on blade geometry configuration and aerodynamic performance. By integrating these two parameterization approaches with multi-objective evolutionary algorithms and Computational Fluid Dynamics (CFD) techniques, we enable global and local optimization of centrifugal compressor blades. The optimization results demonstrate a 1.77% enhancement in isentropic efficiency under rated operating conditions, a 7.8% increase in surge margin, a 1.6% improvement in isentropic efficiency under normal operating conditions, and an 11.8% enhancement in surge margin. Through two optimization stages, the optimization space for blade geometry is thoroughly explored, enhancing solution quality and contributing to the advancement of impeller mechanical optimization design theory.