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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.
A. Chandrakar, A. K. Nayak, Vinod Gopika
Nuclear Technology | Volume 194 | Number 1 | April 2016 | Pages 39-60
Technical Paper | doi.org/10.13182/NT15-80
Articles are hosted by Taylor and Francis Online.
Research in the field of passive system reliability analysis is garnering sharp interest in the nuclear community. Passive systems are being utilized extensively in current- and future-generation reactors for their normal operations as well as for safety critical operations during any accidental conditions. In this paper, we present a methodology called Analysis of Passive System ReliAbility Plus (APSRA+) for evaluating reliability of passive systems. This methodology is an improved version of the existing APSRA methodology. The methodology has been applied to the passive isolation condenser system (ICS) of the AHWR (Advanced Heavy Water Reactor). With the help of the APSRA+ methodology, the probability of the passive ICS failing to maintain the clad temperature under 400°C is estimated to be of the order 1×10−10.
Important features of APSRA+ are the following. First, it provides an integrated dynamic reliability method for the consistent treatment of dynamic failure characteristics such as multistate failure, fault increment, and time-dependent failure rate of components of passive systems. Second, this methodology overcomes the issue of process parameter treatment by just the probability density function or by root cause analysis, by segregating the parameters into dependent and independent process parameters and then giving a proper treatment to each of them separately. Third, the methodology treats the model uncertainties and independent process parameter variations in a consistent manner.
In APSRA+, the important parameters affecting the passive system under consideration are identified using sensitivity analysis. To evaluate the system performance, a best-estimate system code is used with due consideration of the uncertainties in empirical models. A failure surface is generated by varying all the identified important parameters; variation from the nominal values of these parameters affects the system performance significantly. These parameters are then segregated into dependent and independent categories. For dependent parameters, it is attributed that the variations of process parameters are mainly due to malfunction of mechanical components or control systems, and hence, root cause analysis is performed. The probability of these dependent parameter variations is estimated using a dynamic reliability methodology based on Monte Carlo simulation. The dynamic failure characteristics of the identified causal component/system are accounted for in calculating these probabilities. For the treatment of independent process parameters, using APSRA+ suggests adopting and integrating classical data-fitting techniques or mathematical models. In the next steps, a response surface-based metamodel is formulated using the generated failure points. The probability of the system being in the failure zone is estimated by sampling and analyzing a sufficiently large number of samples for all the dependent and independent process parameters based on the probability of variations of these parameters, which were estimated using dynamic reliability methodology.
