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An experimental study has been conducted to investigate the heat transfer characteristics of supercritical carbon dioxide (sCO2) uniformly heated in the horizontal circular smooth tube. The results illustrated that there was a significant difference in heat transfer between the top wall and bottom wall due to the buoyancy. Bulk flow acceleration cannot be negligible in the high heat flux region, which leads to heat transfer deterioration. A new heat transfer correlation is proposed, in which the buoyancy parameter and bulk flow acceleration have been taken into account. The new correlation and six classic correlations for sCO2 are examined in horizontal tubes. The comparison indicates that the new correlation has a better performance for sCO2 flowing through a horizontal heating tube under natural circulation conditions. For example, 94.9% of the calculated results using the new heat transfer correlation were within ±30% of the experimental results while only 87.9% of that using the Jackson correlation (the best of the six) were within the same error bands.

To effectively monitor the variety of distributions of neutron flux, fuel power or temperatures in the reactor core, usually the ex-core and in-core neutron detectors are employed. The thermocouples for temperature measurement are installed in the coolant inlet or outlet of the respective fuel assemblies. It is necessary to reconstruct the measurement information of the whole reactor position. However, the reading of different types of detector in the core reflects different aspects of the 3D power distribution. The feasibility of reconstruction the core three-dimension power distribution by using different combinations of in-core, ex-core and thermocouples detectors is analyzed in this paper to synthesize the useful information of various detectors. A comparison of multilayer perceptron (MLP) network and radial basis function (RBF) network is performed. RBF results are more extreme precision but also more sensitivity to detector failure and uncertainty, compare to MLP networks. This is because that localized neural network could offer conservative regression in RBF. Adding random disturbance in training dataset is helpful to reduce the influence of detector failure and uncertainty. Some convolution neural networks seem to be helpful to get more accurate results by use more spatial layout information, though relative researches are still under way.

Printed Circuit Heat Exchanger (PCHE) is a widely used heat exchanger in the supercritical carbon dioxide (sCO2) Brayton cycle because it can work under high temperature and pressure, and has been a hot topic in Next Generation Nuclear Plant (NGNP) projects for use as recuperators and condensers. Most previous studies focused on channel structures or shapes. However, no clear advancement has so far been seen in the allover size of the PCHE. In this paper, we proposed an optimal size of the PCHE with a fixed volume. Two boundary conditions of PCHE were simulated, respectively. When the volume of PCHE was fixed, the heat transfer rate and pressure loss were picked as the optimization objectives. The Pareto front was obtained by the Multi-objective optimization procedure. We got the optimized number of PCHE channels under two different boundary conditions from the Pareto front. The comprehensive performance can be increased by 5.3% while holding in the same volume. The numerical results from this study can be used to improve the design of PCHE with straight channels.

Several ensemble learning methodologies have been presented to improve the performance of the core power distribution reconstruction, such as boosting technology, bagging technology and stacking technology. Four commonly used core power reconstruction methods, including PEM, TPS, WCM and ANN are adopted as base learners or meta-learners. The power distribution of the control rod drop accident and quadrant power tilt accident, which combine with detectors measurement noise, is reconstructed. The numerical simulation shows that the results of the stacking methodology can be better than any single one of these base learners under large sample spatial variation of power distribution and large measurement uncertainty. Otherwise results that close to the best one of these base learner results could be generated in stacking methods. In bagging and boosting methodologies, for the unstable learner such as TPS and ANN methods with large measurement uncertainty, both methodologies will help to reduce the reconstruction errors. The boosting methodology could even get better results than bagging. While for the stable learner (such as PEM learner, WCM learner) or unstable learner (e.g. the TPS or ANN method) but under small spatial variation (or small measurement uncertainty) that is not sensitive to the change of sample data, the bagging or boosting method even will reduce the reconstruction performance. Generally, ensemble learning methodologies have capability to improve the core abnormal detectability in most operation scene. Usually stacking methodology performs much better than bagging and boosting methodologies.

The core monitoring system (CMS) is one of the most important systems in safety of an operational reactor. The detectors arrangement has a significant effect on the evaluation of the reactor power distribution and thermal hydraulic parameters. In this paper, three optimization methodologies are explored under a specific number of detectors to achieve the appropriate detector arrangement. These methods consist of the knight moving law (KML) method, the random forest (RF) method and the correlation coefficient (CC) method. The information theory is used to evaluate the performance of these optimization methods. At last, the CC method can obtain the best detector arrangement with smallest mutual information. Meanwhile, with RF method it may be difficult to obtain the best solution, comparing with the other methods. Although the information entropy method provides a glimpse of the detector arrangement, a more comprehensive evaluation needs to be further refined in engineering practice, taking into account issues such as the influent of detector failure and detector random error, and sensitivity analysis under more reactor operating conditions.

This paper investigated the influence of various types of spatial interpolation algorithms in the reactor in-core power distribution reconstruction. These algorithms include different kinds of kernel function used in radial basis function (RBF) methods or support vector machine regression (SVR) methods, different orders of polynomial trend surface analysis (TSA), and various forms of distance weight average (DWA) methods, geo-statistics interpolation method. A typical pressurized water reactor core with 157 fuel assemblies and 33 measurement instruments located is analyzed. The validations of these methods under the measurement core status and predictive core status have been provided. The criterions of relative root mean square error (RRMSE) have been applied to guarantee the accuracy of these algorithms. The comparison of the different spatial interpolation algorithms shows that the DWA basis methods usually perform much better and stable than other methods. PEM and SVR methods have very poor performance in high signal deviation situation, but they can effectively eliminate measurement error in the opposite situation. The fitting methods TPS0, PEM4 could not be used in in-core power distribution reconstruction (IPDR). TPS1 is the best choice for no parameter RBF methods. While for other RBF basis methods, optimization algorithm should be used to search the optimized model parameters. The performance of RBF_TPS1, DWA_OK, SVR_Gauss, RBF_Gauss fall into a same group. Three-dimension surfaces of fitting results are compared. The factors are discussed that affect the reconstructed results of the methods, including detectors number, detectors design pattern and detector measurement properties, variability of the fitting surface. Suggestions to select an appropriate spatial interpolator method are provided.

Extended Kalman filter (EKF) and cubature Kalman filter (CKF) are proposed to estimate the state parameters of an external neutron source driven sub-critical reactor, including power level, reactivity, external neutron source, six-groups of delayed neutrons precursor densities, equivalent fuel temperature, average coolant temperature and nuclear densities of iodine, xenon, promethium, samarium nuclides. Parameter settings and matters needed attention in EKF and CKF are also analyzed, especially the relationship between model prediction covariance matrix and measurement covariance matrix. In order to effectively identify the maneuvering of the external neutron source and reactivity on the uncertainty of the prediction model, two adaptive algorithms are proposed to adjust the covariance matrix of the prediction model online. The results show that these two adaptive algorithms can effectively detect various maneuvering such as neutron source variation and reactivity insertion, and realize the optimal estimation of the reactor state using EKF method. However, CKF has divergence and non-convergence. EKF achieves good results in all parameters estimation.