分类： 物理学 >> 核物理学 提交时间： 2023-11-23
摘要： The supercritical CO2 Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout, compact structure, and high cycle efficiency. Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy. Parametric analysis, multi-objective optimizations, and four decision-making methods are applied to obtain each Brayton schemes optimal thermohydraulic and economic indexes. Results show that for the same design thermal power scale of reactors, the higher the cores exit temperature, the better the Brayton cycles thermo-economic performance. Among the four-cycle layouts, the recompression cycle (RC) has the best overall performance, followed by the simple recuperation cycle (SR) and the intercooling cycle (IC), and the worst is the re-heating cycle (RH). However, RH has the lowest total cost of investment (Ctot) of $1619.85 million, and IC has the lowest levelized cost of energy (LCOE) of 0.012$/(kWh). The nuclear Brayton cycle systems overall performance has been improved due to optimization. The performance of the Molten Salt Reactor combined with the intercooling cycle (MSR-IC) scheme has the greatest improvement, with the net output power (Wnet), thermal efficiency t, and exergy efficiency (e) improved by 8.58%, 8.58%, and 11.21% respectively. The performance of the Lead-cooled Fast Reactor combined with the simple recuperation cycle scheme was optimized to increase Ctot by 27.78%. In comparison, the internal rate of return (IRR) increased by only 7.8%, which is not friendly to investors with limited funds. For the nuclear Brayton cycle, the Molten Salt Reactor combined with the recompression cycle scheme should receive priority, and the Gas-cooled Fast Reactor combined with the re-heating cycle scheme should be considered carefully.
分类： 核科学技术 >> 裂变堆工程技术 提交时间： 2023-06-01
摘要： Recent reactor antineutrino experiments have observed that the neutrino spectrum changes with the reactor core evolution and that the individual fissile isotope antineutrino spectra can be decomposed from the evolving data, providing valuable information for the reactor model and data inconsistent problems. We propose a machine learning method by building a convolutional neural network based on a virtual experiment with a typical short-baseline reactor antineutrino experiment configuration: by utilizing the reactor evolution information, the major fissile isotope spectra are correctly extracted, and the uncertainties are evaluated using the Monte Carlo method. Validation tests show that the method is unbiased and introduces tiny extra uncertainties.
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