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There is renewed interest in developing small modular reactors andmicro-reactors. Innovation is necessary in both construction and operationmethods of these reactors to be financially attractive. For operation, an areaof interest is the development of fully autonomous reactor control. Significantefforts are necessary to demonstrate an autonomous control framework for anuclear system, while adhering to established safety criteria. Our group hasproposed and received support for demonstration of an autonomous framework on asubcritical system: the MIT Graphite Exponential Pile. In order to have a fastresponse (on the order of miliseconds), we must extract specific capabilitiesof general-purpose system codes to a surrogate model. Thus, we have adoptedcurrent state-of-the-art neural network libraries to build surrogate models.  This work focuses on establishing the capability of neural networks toprovide an accurate and precise multi-dimensional regression of a nuclearreactor's power distribution. We assess using a neural network surrogateagainst a previously validated model: an MCNP5 model of the MIT reactor. Theresults indicate that neural networks are an appropriate choice for surrogatemodels to implement in an autonomous reactor control framework. The MAPE acrossall test datasets was < 1.16 % with a corresponding standard deviation of <0.77 %. The error is low, considering that the node-wise fission power can varyfrom 7 kW to 30 kW across the core.

Deep Surrogate Models for Multi-dimensional Regression of Reactor Power