Pradeep Ramuhalli

Austenitic stainless steels are subject to the precipitation of chromium carbides (M23C6) during exposure to high temperatures, causing these alloys to be susceptible to intergranular corrosion due to chromium depletion along grain boundaries. The acoustic nonlinearity parameter, β, shows sensitivity to the formation of carbides in these alloys. The Thermo-Calc TC-PRISMA module was used to model the nucleation and growth of grain boundary M23C6 carbides. The model was verified with scanning transmission electron microscopy analysis that allowed measurements of the grain boundary precipitates. The paper introduces a reduced-order model of the acoustic nonlinearity based on the formation of misfit dislocations at the interface of the grain boundary precipitate and matrix to explain the change in β during isothermal aging. A direct relationship between the radius of the M23C6 grain boundary carbides and β was observed and verified with nonlinear ultrasound measurements on 304L and 316L stainless steels.

Sustainable nuclear power to promote energy security is a key national energy priority. The development of small modular reactors (SMRs) is expected to provide the United States with an economically viable energy option that supports this priority. Small modular reactors (SMR) are typically defined as nuclear reactors that have electrical output less than about 300 MWe [1]. In recent years, SMRs are seeing renewed interest due to several factors: 1. Economy of scale. Modular or grid-appropriate reactors can be used to expand power plants to meet needs [2], resulting in potential economies of scale that larger reactors cannot easily provide. 2. Ease of fabrication/construction. The forging capabilities for the smaller reactor pressure vessels and piping necessary for SMRs are more readily available. This also enables potentially faster and easier construction of SMRs. 3. Usually grid-following. SMRs can ramp up or down production of electricity as needed as the modular design allows for better control of grid-appropriate reactors. 4. Improved safety characteristics. Most current SMR designs rely on passive rather than active safety systems.

The nuclear power industry has recently proposed using ultrasonic testing (UT) in lieu of radiographic testing (RT) on new construction welds and on the repairs of operating reactor component welds. Advantages of UT include a reduction in inspection time, costs, and plant personnel exposure to radiation fields. The replacement of one nondestructive testing method with another, however, requires a detailed analysis of the capabilities of the replacement method as compared to the existing method. Capabilities of interest in this context include detection reliability, false call rates, flaw characterization and sizing accuracy, human factors, data recording capabilities, and cost. The interchangeability of UT and RT has been studied by several institutions, but the evidence found to date in a literature search is not conclusive and therefore requires further investigation to assess the ability of UT to meet nuclear power industry fabrication and pre-service inspection standards. This paper reviews relevant literature on the interchangeability of UT and RT, and identifies potential gaps that may need to be addressed in this area.

Corrosion quantification is an important aspect of aircraft skin NDE. Eddy current and pulsed-eddy current techniques are frequently used to measure corrosion. Corrosion quantification from these NDE measurements is typically an ill-posed inverse problem. A sequential Monte Carlo (SMC) based technique is proposed here to solve this inverse problem by fusing multiple NDE measurement modes. Inversion results are compared with digital x-ray thickness mapping to quantify the efficacy of the proposed technique.

Magnetic flux leakage (MFL) methods are commonly used in the nondestructive evaluation (NDE) of ferromagnetic materials. An important problem in MFL NDE is the determination of flaw parameters such as the flaw length, depth, and shape (profile) from the measured values of the flux density B. Commonly used methods use a forward model in a loop to determine B for a given set of flaw parameters. This approach iteratively adjusts the flaw parameters to minimize the error between the measured and predicted values of B. This article proposes the use of neural networks as forward models. The proposed approach uses two neural networks in feedback configuration—a forward network and an inverse network. The second network is used to predict the profile given the measured value of B, and acts to constrain the solution space. Results of applying these methods to MFL data obtained from a two-dimensional finite-element model, with rectangular flaws of various dimensions, are presented.