| | https://neup.inl.gov/SiteAssets/FY%202018%20Abstracts/CFA-18-15141_TechnicalAbstract_2018CFATechnicalAbstract18-15141.pdf | 18 | Argonne National Laboratory | Nuclear Energy Enabling Technologies R&D (NEET) | Nuclear Energy Enabling Technologies (NEET) | $1,000,000.00 |
This project aims to develop and demonstrate a novel pulsed thermal tomography (TT) non-destructive examination (NDE) method for in-service inspection of additively manufactured (AM) reactor components and materials. NDE capability developed in this project will accelerate deployment of components produced with AM techniques in commercial nuclear reactors.
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| | https://neup.inl.gov/SiteAssets/FY%202018%20Abstracts/CFA-18-15179_TechnicalAbstract_2018CFATechnicalAbstract18-15179.pdf | 18 | Argonne National Laboratory | Nuclear Energy Enabling Technologies R&D (NEET) | Nuclear Energy Enabling Technologies (NEET) | $1,000,000.00 |
This project will develop and demonstrate data-analytic methods to address the problem of how to assign a sensor set in a nuclear facility such that 1) a requisite level of process monitoring capability is realized, and in turn, 2) the sensor set is sufficiently rich to allow analytics to determine the status of the individual sensors with respect to their need for calibration. This approach will allow for automated calibration status, avoiding unneeded calibration activities in the facility.
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| | https://neup.inl.gov/SiteAssets/FY%202018%20Abstracts/CFA-18-15233_TechnicalAbstract_2018CFATechnicalAbstractCFA-18-15233.pdf | 18 | Idaho National Laboratory | Nuclear Energy Enabling Technologies R&D (NEET) | Nuclear Energy Enabling Technologies (NEET) | $1,000,000.00 |
This project will apply advanced sensor technologies, particularly wireless sensor technologies, and data science-based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants, and improve plant performance. The resulting technology is expected to improve plant economics by enabling the transition from periodic maintenance to predictive maintenance. Predictive maintenance will allow plants to better prepare for upcoming maintenance activities by optimizing allocation of resources including tools and labor.
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| | https://neup.inl.gov/SiteAssets/FY%202018%20Abstracts/CFA-18-15086_TechnicalAbstract_2018CFATechnicalAbstract15086.pdf | 18 | The Ohio State University | Nuclear Energy Enabling Technologies R&D (NEET) | Nuclear Energy Enabling Technologies (NEET) | $1,000,000.00 |
This project aims to build and test an optical fiber based gamma thermometer (OFBGT) using two university research reactors, and to develop methods to process the data that is produced by OFBGTs to produce estimates of the power density in the volume of the reactor that surrounds the OFBGTs. The OFBGT sensor will be robust and resilient, and capable of producing 'big data' scale information, with the smallest possible sensor footprint in the core.
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| | https://neup.inl.gov/SiteAssets/FY%202018%20Abstracts/CFA-18-15251_TechnicalAbstract_2018CFATechnicalAbstractCFA-18-15251.pdf | 18 | University of Pittsburgh | Nuclear Energy Enabling Technologies R&D (NEET) | Nuclear Energy Enabling Technologies (NEET) | $1,000,000.00 |
This project aims to develop and establish an innovative approach to drastically reduce development and post-processing costs associated with laser powder bed additive manufacturing (AM) of complex nuclear reactor components with internal cavities and overhangs. The proposed innovative approach integrates dissolvable supports, topology optimization, and microstructure design to achieve the project goal. Using optimally designed dissolvable supports, this research will make state-of-the-art nuclear components much cheaper, have minimal distortion, and could eliminate build failures altogether.
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