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Latest News
Colin Judge: Testing structural materials in Idaho’s newest hot cell facility
Idaho National Laboratory’s newest facility—the Sample Preparation Laboratory (SPL)—sits across the road from the Hot Fuel Examination Facility (HFEF), which started operating in 1975. SPL will host the first new hot cells at INL’s Materials and Fuels Complex (MFC) in 50 years, giving INL researchers and partners new flexibility to test the structural properties of irradiated materials fresh from the Advanced Test Reactor (ATR) or from a partner’s facility.
Materials meant to withstand extreme conditions in fission or fusion power plants must be tested under similar conditions and pushed past their breaking points so performance and limitations can be understood and improved. Once irradiated, materials samples can be cut down to size in SPL and packaged for testing in other facilities at INL or other national laboratories, commercial labs, or universities. But they can also be subjected to extreme thermal or corrosive conditions and mechanical testing right in SPL, explains Colin Judge, who, as INL’s division director for nuclear materials performance, oversees SPL and other facilities at the MFC.
SPL won’t go “hot” until January 2026, but Judge spoke with NN staff writer Susan Gallier about its capabilities as his team was moving instruments into the new facility.
Bin Long, Ying Liu, Fulin Zeng, Jijun Zhou, Yuqian Yang
Fusion Science and Technology | Volume 78 | Number 5 | July 2022 | Pages 379-388
Technical Paper | doi.org/10.1080/15361055.2022.2033061
Articles are hosted by Taylor and Francis Online.
Edge-coherent mode (ECM) is one of the most promising modes in the tokamak fusion experiment, such as the Experimental Advanced Superconducting Tokamak (EAST). This paper presents an efficient convolution neural network model called NoiseNet for ECM recognition from the cross-power spectral data. NoiseNet suppresses the overfitting by applying noise in both the horizontal and vertical directions to the output of each layer of the convolution. And the improvement of the receptive field enables the convolution layer to better learn the difference between the ECM and the turbulence in the data. Experiments show that NoiseNet has better performance in ECM recognition with fewer parameters, and thus improved efficiency, than other major models, such as AlexNet, ResNet, and DenseNet. NoiseNet achieves a test accuracy of 93.94% on the ECM data sets. In addition, compared with the traditional method, this method does not depend on the empirical threshold and its generalization ability will improve with the increase in the amount of data.
