Chao Guo

Design and Development Framework of Safety-Critical Software in HTR-PM

With the development of information technology, the instrumentation and control system of nuclear power plant nowadays rely heavily on the massive and complex software to ensure the safe and efficient operation of the power plant. The improvement of the software design and development for the safety systems has been a research focus for its decisive impact on the nuclear safety. The framework of the software design and development for reactor protection system in High Temperature Gas-Cooled Reactor-Pebble bed Module was introduced in this paper. Firstly, during the design period, in addition to multichannel redundancy, grouping of protection variables and diverse 2-out-of-4 logics were adopted by different subsystems of each channel in case of common cause failure. Then a series of development characteristics together with strict software verification and validation were performed. Thirdly, during the software test period, an improved software reliability growth model based on the Goel-Okumoto model according to the analysis of fault severity was proposed to help in estimating the reliability of the software product and identifying the software release time.

Software reliability growth model of reactor protection system based on ratio analysis of critical faults

The application of digital instrumentation and control systems in Nuclear Power Plants (NPPs) provides a series of advantages, but it also raises challenges in the reliability analysis of safety-critical systems in the NPPs. Software testing is one of the most significant processes to assure software reliability, and the safety-critical systems of NPPs are sensitive to the severity of software faults, especially the critical faults that infect the system function greatly. Previous software reliability models related to the analysis of fault severity were mostly based on the assumption of different severities. In this paper, the fault severity data collected during the software test process were used for modeling the test process with a Software Reliability Growth Model based on a non-homogeneous Poisson process. The mean value function was derived by considering the ratios of critical and non-critical faults and was named as “Ratio of Critical-Faults model” (RCF model). The fault data collected while developing the safety-critical system were used to validate this model. According to the analysis, RCF model had fitting abilities similar to that of the Goel-Okumoto model and Inflection S-shaped model whereas the prediction effect of the RCF model was better than that of these two models, especially when little data were collected, which could be used to determine the release time of the software.

Reliability analysis for the reactor protection system of HTR-PM

A high-temperature gas-cooled reactor-pebble bed module (HTR-PM), as a demonstration nuclear power plant (NPP), is now under construction in the Shandong province of China. The reactor protection system (RPS) in an HTR-PM is a safety-related system and is mainly in charge of monitoring safety-related parameters according to the reactor protection requirements. Thus, an RPS is very important to operations during the entire life cycle of an NPP. An RPS for an HTR-PM is completely designed and developed in China, which owns all intellectual property rights. Traditionally, the reliability of an RPS is evaluated based on a failure mode, effects, and criticality analysis (FMECA) and a fault tree analysis (FTA), which are static methods and cannot reflect the reliability of the systems dynamics. In this paper, we propose a dynamic reliability model for the RPS in an HTR-PM based on the Markov chain theory. Using this model, all states and their dynamic transition processes, especially the degradation process from a 2-out-of-4 structure to a 2-out-of-3 structure, are all determined. Moreover, based on this proposed model, an optimal surveillance test interval can be determined.

Software reliability modeling with fault detection data when knowing fault severity

Software Reliability Growth Models (SRGMs) are commonly used to estimate the software quality in software engineering. Currently, most SRGMs employ the fault number data collected during software fault detection process and model the fault number data with corresponding detection time. In this process, fault severity is generally used as an unknown parameter to be solved by the modeling process. Few articles incorporate the fault severity as a known factor for the fault-detection-process modeling. In fact, each fault detected is classified into different severities during software testing process in a lot of software testing projects, that is, the fault severity can be treated as a known factor. Generally, the higher severity a fault has, the larger effect it may create. Therefore, besides the total fault count remained in software, the number of remained faults in different severity, especially the faults that may cause serious consequences, is more critical to the system operation. Incorporating the data information, we proposed one novel nonhomogeneous Poisson process software reliability growth model in this article, which involves both the failure time and the severity of each fault into modeling. In this article, we first discussed how to introduce the severity into modeling. In actual software development process, it has been observed that the fault in trivial severity is detected more easily and less influence by the learning effect than the fault in hard severity. Thus, we proposed a Severity Ratio Function (SRF) to describe the percentage of the fault detection rate in same severity out of the total fault detection rate changing in time. Then, based on the SRF, a new software reliability model is derived. Finally, this model are evaluated and validated on actual test data set collected from a nuclear power plant protection system. The results of numerical illustration demonstrate that the proposed MVF provide better estimation and fitting under comparisons.