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Halsey, W.

Publications and source records attributed to Halsey, W..

Nuclear Waste Attributes of SMRs Scheduled for Near-Term Deployment

The purpose of this study is to evaluate the nuclear waste attributes of Small Modular Reactors (SMRs) scheduled for deployment within this decade using available data and established nuclear waste metrics, with the results compared to a reference large Pressurized Water Reactor (PWR). The current fleet of commercial nuclear reactors in the U.S. is composed of 92 large Light Water Reactors (LWR) with an average electricity generating capacity of over 1,000 MWe each. These large LWRs built on-site in massive construction projects have been the mainstay of the industry for the last 50 years. However, new construction soon is expected to include several designs of smaller reactors primarily fabricated in factories and installed in the field in modules. Some of these SMRs will also be LWRs, while some will use other coolants such as liquid metals, molten salts or gases, and different types of fuels. The technologies and economics of SMRs have been the focus of many studies, but there has been only minimal information published on the amount of nuclear waste different types of SMRs are expected to generate and no reports focused on near-term-deployable designs. In this study, the nuclear waste attributes of three small reactors scheduled for near-term-deployment, VOYGR TM (from NuScale Power), Natrium TM ab (from TerraPower), and Xe-100 (from X-energy), were assessed by comparing nuclear waste metrics with those of a reference large Pressurized Water Reactor (PWR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

A stochastic scan strategy for grain structure control in complex geometries using electron beam powder bed fusion

Spatial control of microstructure within a three-dimensional component has been a dream of materials scientists for centuries. However, limitations in traditional manufacturing processes prevent detailed control over the distribution of microstructures in a single part. Here, we demonstrate the ability to control grain structure and crystallographic texture during metal additive manufacturing for arbitrary cross-sections of a practical size, with profound implications for the design and optimization of next-generation products. The key to this advance is a new geometry agnostic scan path algorithm that manipulates the spatial distribution of solidification conditions. Utilizing a fundamental understanding of solidification dynamics and a model of the heat transfer during processing, we have designed this algorithm to manipulate the natural competition between epitaxial dendrite growth and grain nucleation. With this algorithm, we successfully controlled the grain structure of Ni-based superalloy IN718 in the shape of the Mona Lisa.

36 MATERIALS SCIENCE↗