西屋电气公司（Westinghouse Electric Corporation），1886年1月8日由乔治·威斯汀豪斯在美国宾夕法尼亚州创立。总部设在宾夕法尼亚州匹兹堡市。1889年时曾改名西屋电工制造公司（Westinghouse Electric Manufacturing Company），1945年10月改用现名。 西屋电气公司曾是世界500强企业，每年营业额超过百亿美元，广泛活跃在能源、交通、通讯、军事、航天、环境健康管理领域。全世界五大洲数以亿计的家庭，商用建筑，政府部门都在使用Westinghouse的产品和服务。 西屋电气公司是美国主要电气设备制造商和核子反应器生产者工厂。主要业务领域涉及发电设备、输变电设备、用电设备和电控制设备、电子产品等门类共4000多种产品。其中，以发电设备、输变电设备尤具特色，从公司成立以来，一直享誉世界 。
The nuclear industry has fully embraced the development of accelerated fuel qualification (AFQ) approaches to speed up the assessment and validation of new fuel designs with respect to performance and safety metrics. To support the AFQ approach to shortening the time to develop and qualify new fuel for higher plant performance, Westinghouse utilizes advanced modeling and simulation technologies as part of their integrated and comprehensive AFQ vision through improved fuel performance prediction under various operating conditions and accident scenarios.This paper provides example applications, prioritized in Westinghouse using machine learning technology, for fuel thermal-hydraulic applications with methodologies that are under development for the prediction of critical heat flux for pressurized water reactor (PWR) fuel thermal margin assessment and surrogate model development for crud-induced power shift risk prediction to enhance PWR fuel operation performance.
Binder jet 3D printing is a promising metal and ceramic additive manufacturing technique for producing stress-free, support-free, never-melted parts. However, binder jetting lacks accessibility for many researchers due to the need to fill large powder beds with a single composition of powder that has high flowability. This paper presents a simple design for a manual binder jet printer that requires only small amounts of powder to operate and allows for multiple compositions to be loaded during the same print to enable layered compositions and properties. Two variations on the design are presented: (1) a lower-cost, lower-precision dosed sprayer version and (2) a higher-cost, higher-precision handheld inkjet printer version. Both versions of the presented binder jet 3D printer can be fabricated with access to a basic machine shop and polymer 3D printing capabilities.
Small modular reactors (SMRs) are gaining popularity due to several economic and safety benefits, along with their desirable load-following capability, which allows them to complement intermittent renewable energy sources. However, the impact of load-following operation on fuel performance remains underexplored. This study uses the fuel performance code, TRANSURANUS, to assess the effects of such operations on pressurized water reactor (PWR) fuel performance, given the design similarities between many SMRs and large light water reactors (LWRs). Hypothetical load-following operations were simulated using varying linear heat rate levels, neutron flux, and coolant flow rate, in accordance with a parameterized load-following operation. Most fuel performance parameters remained below safety limits. However, in some cases, the pellet–cladding interaction–stress corrosion cracking (PCI–SCC) model (SPAKOR) predicted cladding cracks and failures that were not observed during regular reference operation. A sensitivity analysis of load-following parameters indicated minimal deviations in fuel performance, apart from the PCI–SCC-related issues.
Three-dimensional digital image correlation (3D-DIC) is a non-contact monitoring technique that is able to provide accurate three-dimensional strain and displacement measurements. Previous research has shown that 3D-DIC can detect micron-scale cracks in structures as they emerge; however, because 3D-DIC is an optical sensing technique, unfavorable visual conditions due to high heat, large deformations, or a significant distance between the structure and the 3D-DIC cameras can make crack detection difficult or impossible. This research aims to develop machine learning algorithms capable of detecting characteristic crack signals in these scenarios. Localized point velocities obtained via 3D-DIC were transformed into 2D color images for machine learning segmentation. A novel dataset processing technique was utilized to produce the training dataset, which overlayed simplistic crack analogs on top of the first 50 images from the test. Different parameters from this technique were investigated to determine their effect on the model’s accuracy and sensitivity. The resulting model detected the onset of significant cracking with an accuracy comparable to acoustic emissions sensors. Varying the processing parameters yielded models that could detect evidence of cracking earlier, at the cost of potentially higher false positive rates. The model also performed well on structures imaged in similar testing setups that were not included in the training dataset. This data processing technique enables crack detection in scenarios where acoustic emissions and other sensors cannot be used. It additionally allows processes already utilizing 3D-DIC to obtain additional information about material performance during testing or operation.
