The Advanced Accelerator Applications (AAA) Program was initiated in fiscal year 2001 (FY-01) by the U.S. Congress, the U.S. Department of Energy (DOE), and the Los Alamos National Laboratory (LANL) in partnership with other national laboratories. The AAA Project and the R&D for its underlying science and technology will require a large cadre of educated scientists and trained technicians in the future. In addition, other applications of nuclear science and engineering (e.g., proliferation monitoring and defense, nuclear medicine, safety regulation, industrial processes, and many others) require increased academic and national infrastructure and student populations. Thus, the DOE AAA Program Office has begun a multi-year program to involve university faculty and students in various phases of the Project to support the infrastructure requirements of nuclear energy, science and technology fields as well as the special needs of the DOE transmutation program. Herein I summarize the goals and accomplishments of the university programs that have supported the AAA Project during FY-01, including the involvement of more than eighty students.

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The primary objective of damage detection is to ascertain with confidence if damage is present or not within a structure of interest. In this study, a damage classification problem is cast in the context of the statistical pattern recognition paradigm. First, a time prediction model, called an autoregressive and autoregressive with exogenous inputs (AR-ARX) model, is fit to a vibration signal measured during a normal operating condition of the structure. When a new time signal is recorded from an unknown state of the system, the prediction errors are computed for the new data set using the time prediction model. When the structure undergoes structural degradation, it is expected that the prediction errors will increase for the damage case. Based on this premise, a damage classifier is constructed using a sequential hypothesis testing technique called the sequential probability ratio test (SPRT). The SPRT is one form of parametric statistical inference tests, and the adoption of the SPRT to damage detection problems can improve the early identification of conditions that could lead to performance degradation and safety concerns. The sequential test assumes a probability distribution of the sample data sets, and a Gaussian distribution of the sample data sets is often used. …

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