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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.
M. Santos, A. J. Cantos
Fusion Science and Technology | Volume 58 | Number 2 | October 2010 | Pages 706-713
Selected Paper from the Sixth Fusion Data Validation Workshop 2010 (Part 1) | doi.org/10.13182/FST10-A10895
Articles are hosted by Taylor and Francis Online.
In the analysis and classification of signals from massive databases, it is highly desirable to use automatic mechanisms. The synergy of artificial intelligence and advanced signal processing techniques is becoming very efficient in developing this kind of task. In this work we employ a signal processing strategy based on the wavelet transform and then genetic algorithms for classification purposes. An in-depth analysis of the waveforms has been carried out, and an analytical preprocessing has been applied to prepare the signals for their classification. Each individual of the simulated population represents a classifying rule, composed of an antecedent and a consequent. The codification of the knowledge is one of the main contributions of this paper. This genetic classification system has been applied to six different classes of plasma signals of the TJ-II stellarator database at CIEMAT in Spain with satisfactory results.
