Design and Characterization of the Modified Purdue Subcritical Pile for Nuclear Research Applications

First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring the fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize the neutron flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5 × 10⁵ cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive data set with the potential for machine learning applications. The neutron flux throughout the pile is measured using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistically accurate Monte Carlo model using OpenMC 0.15.0 and MCNP 6.3. The experimental measurements reveal that the testbed has a neutron environment with a total neutron flux approaching 9.5 × 10³ n/cm² × s and a thermal flux of 6.5 × 10³ n/cm² × s. This work establishes that the modified Purdue subcritical pile can provide fair neutron and gamma fluxes within a large volume to enable the radiation testing of integral electronic components and can be a versatile research instrument with the potential to support material testing and limited isotope activation, while generating valuable training data sets for machine learning algorithms in nuclear applications.