The EU-CHANCE project aims at the issue of the characterization of conditioned radioactive waste (CRW) and one objective of CHANCE is to focus on: Calorimetry as a comprising non-destructive technique to reduce uncertainties on the inventory of radwaste containing shielded and hidden material difficult to be measured by other means. A MCNP6-based numerical study comprising the particle flux out of a 200L mock-up drum in a Large Volume Calorimeter (LVC) currently manufactured by KEP Nuclear (France) will be presented and discussed. For the analyses, the particle flux and energy deposition in each layer of the calorimeter were determined. The results yield that a significant fraction of the radiation would leave the system and not contribute to the measurable heat deposition. The expected energy deposition is obtained and cumulated for each layer over the whole energy range revealing the fraction of particles actually escaping the LVC calorimeter. While this escape fraction needs and can be determined, the LVC is a very suitable apparatus for the anticipated experiments on large and heterogeneous waste drums that possibly contain deeply buried beta-emitters (e.g. Sr/Y-90) or shielded alpha-sources hidden inside the drum with a significant level of gamma and neutron radiation background radiation. The high-energy part of this gamma and neutron flux may even reach the reference chamber of the calorimeter and deposit some energy there, compromising the calibration and may cause a double-bias.
The MARIA research reactor is designed and operated as a multipurpose nuclear installation, combining material testing, neutron beam experiments, and medical and industrial radionuclide production, including molybdenum-99 (99Mo). Recently, after fuel conversion to LEU and rejuvenation of the staff while maintaining their experience, MARIA has been used to respond to the increased interest of the scientific community in advanced nuclear power studies, both fission and fusion. In this work, we would like to introduce MARIA’ s capabilities in the irradiation technology field and how it can serve future nuclear research worldwide.
The study demonstrates an application of genetic algorithms (GAs) in the optimization of the first core loading pattern. The Massachusetts Institute of Technology (MIT) BEAVRS pressurized water reactor (PWR) model was applied with PARCS nodal-diffusion core simulator coupled with GA numerical tool to perform pattern selection. In principle, GAs have been successfully used in many nuclear engineering problems such as core geometry optimization and fuel configuration. In many cases, however, these analyses focused on optimizing only a single parameter, such as the effective neutron multiplication factor (k eff), and often limited to the simplified core model. On the contrary, the GAs developed in this work are equipped with multiple-purpose fitness function (FF) and allow the optimization of more than one parameter at the same time, and these were applied to a realistic full-core problem. The main parameters of interest in this study were the total power peaking factor (PPF) and the length of the fuel cycle. The basic purpose of this study was to improve the economics by finding longer fuel cycle with more uniform power/flux distribution. Proper FFs were developed, tested, and implemented and their results were compared with the reference BEAVRS first fuel cycle. In the two analysed test scenarios, it was possible to extend the first fuel cycle while maintaining lower or similar PPF, in comparison with the BEAVRS core, but for the price of increased initial reactivity.
This work presents a demonstrational application of genetic algorithms (GAs) to solve sample optimization problems in the generation IV nuclear reactor core design. The new software was developed implementing novel GAs, and it was applied to show their capabilities by presenting an example solution of two selected problems to check whether GAs can be used successfully in reactor engineering as an optimization tool. The 3600 MWth oxide core, which was based on the OECD/NEA sodium-cooled fast reactor (SFR) benchmark, was used a reference design [1]. The first problem was the optimization of the fuel isotopic inventory in terms of minimizing the volume share of long-lived actinides, while maximizing the effective neutron multiplication factor. The second task was the optimization of the boron shield distribution around the reactor core to minimize the sodium void reactivity effect (SVRE). Neutron transport and fuel depletion simulations were performed using Monte Carlo neutron transport code SERPENT2. The simulation resulted in an optimized fuel mixture composition for the selected parameters, which demonstrates the functionality of the algorithm. The results show the efficiency and universality of GAs in multidimensional optimization problems in nuclear engineering.
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