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Monte Carlo N-Particle (MCNP) is a widely-used code in nuclear engineering, but it needs high computation times due to the tracking of every single particle and interaction event. This causes shielding optimization using trial-and-error to take long times to complete. It can be solved by using multiprocessing, but this requires the MCNP source code which can be difficult to obtain. Therefore, this paper aims to suggest a solution on how Python can be used to run multiple instances of the code in shielding optimization. Two hardware setups were tested: one with a dual-core CPU, and one with a six-core CPU. Each of them would run several repeated simulations with and without multiprocessing enabled. Comparisons were made between each case of the same setup to observe the improvements in completion time. A tutorial of the Python algorithm is also provided in the methodology.

Mixed neutron and gamma radiations are common in many nuclear applications. Several materials can be combined to obtain a composite material that is better for mixed radiation than the individual component materials. The aim of this paper is to investigate the shielding effectiveness of a polyethylene (PE)-based composite with boron and tungsten additives. Several compositions are tested to shield against a 252Cf fission neutron source using an attenuation experiment. The composite is also manufactured using the melt-mixing method of component raw materials. Comparisons are made between the different compositions and the experimental results. Results suggest that the PE composite with 16%wt boron and 16%wt tungsten show the best mixed radiation attenuation as compared to pure PE, PE composite with 25%wt boron, and PE composite with 25%wt tungsten.