Non-uniform fuel distribution and thermo-mechanical analysis of a 1 MW thermal power micronuclear heat pipe reactor.

One of the goals in improving the design of compact portable micronuclear heat pipe reactors is to enhance their operating life so that they can generate maximum power within safe nuclear, thermal, and mechanical limits and with minimal human intervention. This work carries out an analysis to estimate the effect of non-uniform fuel enrichment and thermo-mechanical performance of a 1 MW thermal power uranium nitride fueled Micro Nuclear Heat Pipe Reactor (MNHPR). For neutronic and thermo-mechanical analyses, the open-source Monte Carlo code OpenMC and the COMSOL Multiphysics codes are used. The neutron flux distribution and subsequent fuel temperature, heat transport, stresses and strains are estimated. The analysis of core power distribution shows an uneven power distribution resulting in hot spots. The maximum fuel centerline temperature of 1353 K at the highest peaking factor 1.22 is within the safety limit. However, the high temperature results in higher thermal stress and subsequent displacement of 119 µm that exceeds the 100 µm fuel-clad gap. Power peaking thus significantly limits the maximum allowed operating power. In this study it is found that non-uniform placement of the fuel reduces power peaking and enhances the overall core performance. It is recommended to consider each fuel ring as a separate zone and gradually change the fuel enrichment in each zone. The non-uniform distribution of the fuel follows the gradual increase of enrichment from ring 1 to ring 5 with max enrichment in ring 5, and then a drop in the enrichment to mitigate any peaking in ring 6 due to its proximity to the reflector. From ring 1 to ring 6 fuel of 60-62-70-70-75-65 percent enrichment is recommended. The proposed fuel strategy mitigates power peaking in the core and enhances the maximum safe operating power level by 15 % from 775 kW to 893 kW without physical design change.

Non-Intrusive Load Monitoring of Buildings Using Spectral Clustering.

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices' pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.