scholarly journals Optimisation of AGR-Like FHR Fuel Assembly Using Multi-Objective Particle Swarm Algorithm

2021 ◽  
Vol 2 (1) ◽  
pp. 35-43
Author(s):  
Marat Margulis ◽  
Eugene Shwageraus

Utilising molten salt as coolant instead of carbon dioxide in traditional advanced gas-cooled reactors (AGRs) can potentially increase their core power density, simplify the safety case and shorten the time needed for the development of the fluoride-salt-cooled high-temperature reactor (FHR). However, the change of coolant has a strong impact on the system behaviour. Therefore, a new type of fuel assembly is required. However, the design of a new assembly is affected by a wide range of parameters. Systematic search through all the potential configurations is prohibitively computationally expensive. In this work, a multi objective particle swarm optimisation (MOPSO) algorithm is utilised to identify the most attractive candidate configurations for the hybrid AGR-like FHR assembly. The first optimisation step targets basic design parameters such as radius and enrichment of the fuel pins, their number and arrangement. MOPSO is based on the concept of Pareto dominance, which is used to determine the flight direction of the simulated particles. The outcome of the optimisation process provides insight on families of possible solutions, which described by the Pareto front.

2019 ◽  
Vol 118 ◽  
pp. 02005
Author(s):  
Ying Ai ◽  
Yuanjie Gao ◽  
dongsheng Liu

Hybrid electric vehicle fuel consumption and emissions are closely related to its energy management strategy. A fuzzy controller of energy management using vehicle torque request and battery state of charge (SOC) as inputs, engine torque as output is designed in this paper foe parallel hybrid electric vehicle. And a multi-objective mathematical function which purpose on maximize fuel economy and minimize emissions is also established, in order to improve the adaptive ability and the control precision of basic fuzzy controller, this paper proposed an improved particle swarm algorithm that based on dynamic learning factor and adaptive inertia weight to optimize the control parameters. Simulation results based on ADVISOR software platform show that the optimized energy management strategy has a better distribution of engine and motor torque, which helps to improved the vehicle’s fuel economy and exhaust emission performance.


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