温度是核电厂安全运行的重要参数.对全光纤式的光纤珐珀温度传感器开展了研究.在单模光纤端面熔接一段带有空气腔的多模光纤.多模光纤部分构成珐珀干涉腔,利用多模光纤形成的珐珀腔感温.在热光效应和热膨胀效应下,温度的变化引起珐珀腔中光程差的改变.试验结果表明,50℃下腔长为592.0 μm的光纤珐珀温度传感器,在50～200℃温度范围下的温度灵敏度为0.018 11 μm/℃、线性度为0.993 65.通过分析讨论,提出了两种进一步提高光纤珐珀温度灵敏度的方法.这种全光纤式的光纤温度传感器相对毛细管式光纤珐珀温度传感器具有体积更小、结构更简单的优势,在恶劣及空间狭小的环境有广阔的应用前景.

The simplification of simulation inevitably leads to model mismatch. In this paper, a once-through steam generator (OTSG) for a small lead bismuth fast reactor (SLBFR) is established and verified, and the OTSG model is simplified by three different methods. Based on the simplified OTSG model, IMC and IMC-PID controllers are designed to verify the sensitivity of the controller to model mismatch. The results show that the sensitivity of the controller to model mismatch is related to the filter parameters. With the increase in λ, the IMC-PID controller becomes insensitive to model mismatch caused by model linearization, non-minimum phase characteristics, noise and time delay. However, the adaptability to model mismatch sacrifices the sensitivity of the system. When λ is too large, the inertia of the controller is too large, resulting in the deterioration of the fast power regulation. Through the research of this paper, the time domain response approximation method is recommended for OTSG model simplification, and λ is recommended to be between 5 and 10 for feedwater IMC-PID controller.

以开放贮液容器运动为代表的一类欠驱动系统中液体的晃动不仅会对系统的稳定性造成影响,还会严重影响系统响应的精确性、工作效率以及安全性等.现有的大量研究表明,液体晃动抑制控制需要建立严格的数学模型和实时测量系统运行过程中的状态信息,并且对于这一类欠驱动系统中的未建模动态、非线性以及系统在运行中的干扰的处理还存在诸多问题.因此,文章针对这一类欠驱动系统中的各种不确定性,提出基于Lyapunov稳定性的模型参考液体晃动抑制自适应控制方法,实现贮液容器位移的快速精确响应、液体的晃动抑制和系统输入能量最小等运动过程中的多目标的优化,最后通过仿真研究和分析,验证其控制方法的有效性和自适应性.

DS evidence theory can be applied to deal with multi-sensor data and get reliable and accurate decision result of system’s target or object due to its good measurement and reasonable description of system’s uncertainty. In practical application field, DS evidence theory is achieved by two steps, one is the acquisition of the basic probability assignment function (BPA), and the other is the synthesis of multiple evidences that is obtained from multi-sensor data. The existing relevant literatures mainly solve the combination problem of conflict evidences, while lack of systematic research on the acquisition of BPA. In this paper, we focus on the discussion and innovation of the acquisition of BPA. BPA in DS evidence theory is the basic unit to describe the uncertainty of the system and evidences, which fundamentally determines its superiority with respect to probability theory and Bayesian reasoning. Getting a reasonable and effective BPA is the premise of the application of DS evidence theory. Traditional BPA acquisition methods have the limitations like absent priori knowledge and low acquisition accuracy. Aiming to solve the technical difficulties of traditional methods, we put forward a new BPA acquisition algorithm based on the slope correlative degree. The novel algorithm proposed in this paper successfully realizes the reliable descriptions of system’s uncertainty, which is conducive to product the accurate BPAs and the precise fusion results. Experimental results and analyses demonstrate that the innovative algorithm can not only produce reasonable BPAs, which can better fit the sensors’ monitoring data, but also expand the gap between the support degree of different propositions in BPA, which is more beneficial to the synthesis of evidences and the decision fusion of the system. Thus, the novel BPA acquisition algorithm has great application value and research significance.

Compared with large pressurized water reactors (PWRs), small PWRs have flexible operating conditions and complex operating environment, putting forward higher requirements on the power control system. Considering the low model identification accuracy of the widely-used model predictive control algorithm in reactor control, a neural network predictive (NNP) power control method is proposed for small PWRs in this paper. The local models under five typical operating conditions are weighted by a multi-model method to establish a core multi-model system, based on which a global NNP power control system is designed. The neural network is used to identify the core model first, and then a performance criterion function is optimized to determine the optimal control input to the reactor core. Simulation results of a small PWR core under typical transient conditions demonstrate the good load-following performance and strong anti-interference capability of the proposed NNP power control method for small PWRs. (C) 2022 Elsevier Ltd. All rights reserved.

基于现有核安全法规标准的要求,分析核安全级仪控通信网络的设计要求,结合无线通信技术的发展和应用现状,开展无线通信技术应用在核安全级仪控通信网络的初步分析研究,得到三个关键问题以及初步解决途径,以便后续进一步研究.

反应堆控制对象具有非线性和多变量耦合的特点,为了优化控制效果,首要任务是建立较为精确的对象模型.神经网络作为近几年较为前沿的智能算法,对非线性系统的辨识展现了明显的优越性.BP神经网络是一种多层前馈神经网络,文章基于高功率区小范围变负荷的反应堆运行数据,采用BP神经网络进行数据的训练,对训练好的网络进行测试,仿真结果验证了辨识的有效性.

考虑到反应堆堆芯非线性、时变性等特点以及外界扰动情况,传统经典控制方法难以实现全工况内反应堆功率的良好控制.因此,本研究提出了一种反应堆功率的神经网络预测控制方法.本文以国际革新安全反应堆(IRIS)为研究对象,建立堆芯非线性模型,并基于MATLAB/Simulink构建反应堆功率的神经网络预测控制仿真平台.仿真结果表明,所设计的神经网络预测控制器可以实现堆芯入口温度扰动和变负荷工况下反应堆功率的良好控制.