Engineering topics

Kersting, A.

Publications and source records attributed to Kersting, A..

3 records

DOE OSTI2021

NNSA-IAEC Science and Technology Working Group Area V - Waste Management and Subsurface Science, (Q1 FY2021)

The objective of Topic Area V is to evaluate the feasibility and safety of an intermediate-depth borehole for disposal of IAEC nuclear waste. The IAEC nuclear waste is currently stored in above ground and/or in near-surface facilities that provide short-term radiological worker safety but do not provide long-term, full security and safeguards protection for the nuclear material. Disposal of the IAEC nuclear waste several hundred meters below the surface in a sealed borehole would provide long-term security and safeguards due to its depth underground, as well as long-term radiological safety, due to its isolation in a stable geological formation.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

NNSA-IAEC Area V workshop on nuclear waste management and subsurface science

The annual meeting of the AREA V – Waste Management and Subsurface Science topic, took place virtually, using the WebEx platform hosted by LLNL, on the 10th and 11th of November 2020. Project summaries are reported.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Constrained Bayesian optimization of criticality experiments

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. Furthermore, we demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗