Search

The accelerator-based boron neutron capture therapy (AB-BNCT) facility is a cancer treatment assembly based on epithermal neutrons generated by an accelerator, which can be built in hospitals in densely populated cities. BNCT has strict requirements for the neutron fluence rate and various contaminations. In order to meet these requirements, the neutron beam shaping assembly (BSA) needs to be designed. The traditional design method for BSA needs to know the performance of various materials in advance in terms of moderation, alignment, shielding, etc. Many researchers have done a lot of work in this area, but the neutron energy changes due to complex nuclear reactions between neutrons and materials. Since the nuclear reaction cross section is closely related to energy, the judgments on the properties of these materials are mostly qualitative and has only little use for the design of BSA. The traditional method first gives a preliminary plan, and then adjusts the material type and material size according to the theoretical calculation results, it is easy to fall into the local optimum, and it is difficult to obtain the global optimal solution. This process takes a lot of time. Traditional design methods rely heavily on the designer’s experience, which is not a good guide for design work. Genetic algorithm is a random global search and optimization method developed by imitating the biological evolution mechanism. It draws on some ideas in evolutionary biology such as heredity, mutation, natural selection and hybridization, and is a general algorithm for solving search problems. The algorithm has little requirements for the problem to be solved, and only needs the fitness function. The genetic algorithm can deal with multiple individuals in the group at the same time, perform parallel search according to multiple routes, and use the roulette method to make selections. The advantage of genetic algorithm is that the search range is large, which is conducive to global selection. Genetic algorithm also has the characteristics of robustness, parallelism and portability. Genetic algorithm has applications in nuclear power plant fuel management, shielding design and reactor BNCT BSA design.In order to solve the problems existing in traditional methods, based on the 14 MeV cyclotron, an optimal design method of BSA based on genetic algorithm was studied in this paper. The program mainly included genetic algorithm module, fitness calculation module, input file generation module and output file processing module. The results show that the method can realize the multi-objective optimization design and obtain good results efficiently. The program can be modified for design in other related fields of nuclear engineering.

Boron neutron capture therapy (BNCT) is tumor-cell targeted radiotherapy that has significant superiority over conventional radiotherapies. The most neutron beams used for BNCT are from the reactors with high power, new design Miniature Neutron Source Reactor(MNSR) with 45kW with BNCT beam for Suranaree University of Technology(SUT) is being designed and built. According to SUT-MNSR physics design, SUT-MNSR will have the epithermal neutron beam for BNCT treatment. The dose rate distribution in the body should be estimated before SUT-MNSR is used for BNCT clinical trials (Brain tumor). This paper introduces the simulation for SUT-MNSR neutron beam by Monte Carlo N-Particle Transport Code (MCNP), and the establishment of human brain model and physics dose rate distribution in brain tumor by MCNP program.The brain model is established according to the different element in the skin, skull and tissue, the distribution of neutron dose and Gamma dose in the brain model were calculated.

The Ground Delay Program (GDP) relies heavily on the capacity of the subject airport, which, due to its uncertainty, adds to the difficulty and suboptimality of GDP operation. This paper proposes a framework for the joint optimization of GDP key parameters including file time, end time, and distance. These parameters are articulated and incorporated in a GDP model, based on which an optimization problem is proposed and solved under uncertain airport capacity. Unlike existing literature, this paper explicitly calculates the optimal GDP file time, which could significantly reduce the delay times as shown in our numerical study. We also propose a joint GDP end-time-and-distance model solved with genetic algorithm. The optimization problem takes into account the GDP operational efficiency, airline and flight equity, and Air Traffic Control (ATC) risks. A simulation study with real-world data is undertaken to demonstrate the advantage of the proposed framework. It is shown that, in comparison with the current GDP in operation, the proposed solution reduces the total delay time, unnecessary ground delay, and unnecessary ground delay flights by 14.7%, 50.8%, and 48.3%, respectively. The proposed GDP strategy has the potential to effectively reduce the overall delay while maintaining the ATC safety risk within an acceptable level., 1. Introduction Ground Delay Program (GDP) is an important strategy in Air Traffic Flow Management (ATFM), which aims at converting airborne delay into safer and more economic ground delay [1]. [...]