VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D
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Improving core performance by reshuffling/reloading the fuel blocks within the core is one of the in-core fuel management methods with two major benefits: a possibility to improve core life and increase core safety. VHTR is a hexagonal annular core reactor with reflectors in the center and outside the fuel rings (3-rings). With the block type fuel assemblies, there is an opportunity for muti-dimensional fuel bocks movement within the core during scheduled reactor refueling operations. As the core is symmetric, by optimizing the shuffle operation of 1/6th of the core, the same process can be repeated through the remaining 5/6th of the core. VHTR has 170 fuel blocks in the core of which 50 are control rod blocks and are not movable to regular fuel block locations. The reshuffling problem now is to find the best combination of 120 fuel blocks that has a minimized power peaking and/or increased core life under safety constraints among the 120! combinations. For evaluating each LP during the shuffling, a fitness function that is developed from the parameters affecting the power peaking and core life is required. Calculating the power peaking at each step using Monte Carlo simulations on a whole core exact geometry model is a time consuming process and not feasible. A parameter is developed from the definitions of reactivity and power peaking factor called the localized reactivity potential that can be estimated for every block movement based on the reaction rates and atom densities of the initial core burnup at the time of shuffling. The algorithm (ReloPSO) is based on Particle Swarm Optimization algorithm the search process by improving towards the optimum from a set of random LPs based on the fitness function developed with the reactivity potential parameter. The algorithm works as expected and the output obtained has a flatter reactivity profile than the input. The core criticality is found to increase when shuffled closer to end of life. Detailed analysis on the burn runs after shuffling at different time of core operation is required to correlate the estimated and actual values of the reactivity parameter and to optimize the time of shuffle.
Lakshmipathy, Sathish Kumar (2012). VHTR Core Shuffling Algorithm Using Particle Swarm Optimization ReloPSO-3D. Master's thesis, Texas A&M University. Available electronically from