Dynamic fault tree analysis of auxiliary feedwater system in a pressurized water reactor

Fahmy, Rania A.; Gomaa, R.

doi:10.1515/kern-2020-0067

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…A low-cost response mechanism is introduced in DMQT. In addition, DMQT does not need to consider label quantity estimation algorithms, which can reduce recognition time and energy consumption [32]. The energy collection rate is achieved by dividing the collected energy by the charging time.…”

Section: Design Of Anti-collision Algorithm For Multi-sensing Tagsmentioning

confidence: 99%

Wireless Power Supply Sensing Tags Based on Anti-Collision Algorithm for Sensing Systems

2024

IEEE Access

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In wireless sensor networks, sensing tags are battery powered, which also limits the battery life. Therefore, it is particularly important to study self powered sensing tags. At present, passive wireless identification systems can achieve self powered communication through tags.However, wireless power supply sensing systems have problems such as low efficiency and short communication distance. To reduce the power consumption of sensing tags and improve the efficiency of sensing systems, a passive wireless low-power hydrogen concentration sensing system is designed. To further reduce the power consumption of sensing tags, a passive wireless magnetic field intensity sensing system is developed.In addition, to solve the low efficiency of multiple sensing labels in wireless power supply sensing systems, an efficient anticollision algorithm based on dynamic binary query trees is designed. According to the research results, in the cold start indoor environment, the energy collection rate of the sensing tag designed in the research was 0.081mJ/s, and the energy collection efficiency was 25.7%.In single/dual sensor mode, the maximum values of sensing error were 2.1% and 5.2%, respectively. The maximum recognition time of the anticollision algorithm based on dynamic multi base query tree was 2.78s, and the minimum value was 1.31s.The research results can provide methodological and technical support for the improvement of wireless power supply sensing systems, expanding the application range of wireless power supply sensing systems.

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Section: Design Of Anti-collision Algorithm For Multi-sensing Tagsmentioning

confidence: 99%

Wireless Power Supply Sensing Tags Based on Anti-Collision Algorithm for Sensing Systems

2024

IEEE Access

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Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Wang,

2024

Kerntechnik

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The digitalization enhances nuclear power plant (NPP) instrumentation and control (I&C) system performance, while simultaneously introducing safety and reliability challenges. To enhance the stability of distributed control system (DCS), and minimize the dangerous failure rate and safe failure rate of safety system, signal quality bits (SQBs) are widely used in NPP DCS to identify the validity of important signals. However, in the past decade or so, numerous unscheduled turbine shutdowns and/or reactor trips have resulted from incorrect SQB design or settings. Therefore, it is necessary to conduct optimization research on the design methods and setting principles of SQBs, establish an optimization method for DCS application software SQB verification and validation (V&V), then minimize DCS faults attributed to SQB. With the aim, this work conducts an in-depth investigation and analysis of the definition, design, setting and transmission of SQB in NPP. A specific optimization method for software V&V to conduct SQB on NPP safety DCS application software is proposed. Twelve concerns in the SQB V&V work are detailed. The reactor trip system ΔT protection logic is utilized as a case to illustrate the detail implementation process of this optimization method. This work provides an engineering technical reference and operational guidance for SQB software V&V, conducive to ensuring the safe and economical operation of NPPs.

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Assessment of digital development design model using deep learning neural network

2024

IFS

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In the context of China’s cultural and tourism industry, cultural equipment plays a critical role in cultural dissemination, especially in remote areas with harsh road conditions and unique environmental factors. However, the efficiency and stability of manual analysis are significantly challenged by these conditions and the vast yet sparsely collected monitoring data. This study aims to develop a method for extracting valuable information from monitoring data to assess the health status of cultural equipment. We introduce a deep learning-based algorithm that leverages convolutional neural networks (CNNs) to extract local features from multidimensional monitoring indicators and long short-term memory (LSTM) networks to capture time series features, facilitating the classification of cultural equipment’s health status. The algorithm’s effectiveness is demonstrated through simulation results, highlighting its practicality and applicability in real-world scenarios. This research not only provides a novel approach for cultural equipment health assessment but also contributes significantly to the field by addressing the challenges of data analysis in complex environments, underscoring the importance of technological advancements in preserving cultural heritage.

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Dynamic fault tree analysis of auxiliary feedwater system in a pressurized water reactor

Cited by 4 publications

References 8 publications

Wireless Power Supply Sensing Tags Based on Anti-Collision Algorithm for Sensing Systems

Wireless Power Supply Sensing Tags Based on Anti-Collision Algorithm for Sensing Systems

Verification and validation optimization method for signal quality bits in digital control system application software of nuclear power plant

Assessment of digital development design model using deep learning neural network

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