Neutronic Design and Analysis of the Holos-Quad Concept
HolosGen, LLC is proposing a highly innovative micro-reactor concept targeting both civilian and military applications. It consists of an advanced helium-cooled gas reactor using a turbojet-type turbine and compressor to achieve a highly condensed reactor that fits into a commercial ISO container. The Holos-Quad concept being considered for civilian applications will generate 22MWt, using four Subcritical Power Modules (SPMs) that fit into one 40-foot ISO container. It is a high-temperature gas-cooled reactor concept using TRISO fuel distributed in graphite hexagonal blocks, cooled with helium gas at 7MPa and a high outlet temperature. In FY2019, HolosGen, LLC received an ARPA-E MEITNER grant to team up with the Argonne National Laboratory (ANL) design team to demonstrate the feasibility of the Holos-Quad concept in terms of neutronics. In particular, the evaluation study of the Holos design features and alternative options for reactivity control was considered. Through these tasks, the ANL design team has been working on demonstrating the feasibility of the Holos-Quad concept in terms of neutronics to help advance the core design of the Holos-Quad. A rigorous design approach was employed for the Holos-Quad in order to ensure that the input space is fully investigated, and that the best solutions are considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the competitive design objectives: its economic performance is maximized with longer lifetime and its transportability to remote locations is maximized with reduced weight. The design problem also required to identify the operational constraints (in terms of thermal hydraulics, reactivity feedback and shutdown margins) and the input parameters that could be varied. In the second step, a sensitivity analysis was performed to enable preliminary investigation of the input space to identify the correlations among input and output variables. It allowed, for instance, to find input parameters that have relatively poor impact on the core performance and that could be removed from the optimization problem to accelerate its convergence. This sensitivity analysis showed that the design problem defined is highly constrained since only 117 solutions out of 1,000 simulated cores met all the design constraints. The design optimization was performed in the third step employing a genetic algorithm to effectively explore this highly constrained input space and find global optimal solutions. More than 2,500 cores were fully evaluated to converge to a Pareto Frontier of optimum solutions providing best compromise between the life-time target and the total core weight, as shown in Figure 1. The main benefit of this approach was to reduce the human effort and to be able to automatically re-run the optimization problem when changing the design problem. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications.