Radioactive high level waste (HLW) sludge generated as a result of decades of production and manufacturing of plutonium, tritium and other nuclear materials is being removed from storage tanks and processed into a glass waste-form for permanent disposition at the Federal Repository. Characterization of this HLW sludge is a prerequisite for effective planning and execution of sludge disposition activities. The radioactivity of HLW makes sampling and analysis of the sludge very challenging, as well as making opportunities to perform characterization rare. In order to maximize the benefit obtained from sampling and analysis, a recommended list of physical property and chemical measurements has been developed. This list includes distribution of solids (insoluble and soluble) and water; densities of insoluble solids, interstitial solution, and slurry rheology (yield stress and consistency); mineral forms of solids; and primary elemental and radioactive constituents. Sampling requirements (number, type, volume, etc.), sample preparation techniques, and analytical methods are discussed in the context of pros and cons relative to end use of the data. Generation of useful sample identification codes and entry of results into a centralized database are also discussed.

Accelerated cleanup initiatives at the SRS include expediting radioactive sludge processing. Sludge is the highest risk component of waste since it contains the highest concentrations of long-lived radionuclides. The sludge is staged into ''batches'' that are then the feed material to the Defense Waste Processing Facility (DWPF) which vitrifies the waste into a safe form for permanent disposal. The preparation of each batch includes sampling and analysis of the slurried material. The results of the characterization are used as the bases for batch blending and processing decisions. Uncertainty is inherent in the information used for planning. There is uncertainty in the quantity of sludge contained in a tank, the waste composition, and the waste physical properties. The goal of this analysis is to develop the basis for the number of physical samples that should be taken from the slurried waste tank and the number of replicates of laboratory measurements that should be performed in order to achieve a specified uncertainty level. Recommendations for sampling and analysis strategies are made based on the results of the analysis.

A particle swarm optimization (PSO) algorithm is combined with a gradient search method in a model-based approach for extracting interface positions in a one-dimensional multilayer structure from acoustic or radar reflections. The basic approach is to predict the reflection measurement using a simulation of one-dimensional wave propagation in a multi-layer, evaluate the error between prediction and measurement, and then update the simulation parameters to minimize the error. Gradient search methods alone fail due to the number of local minima in the error surface close to the desired global minimum. The PSO approach avoids this problem by randomly sampling the region of the error surface around the global minimum, but at the cost of a large number of evaluations of the simulator. The hybrid approach uses the PSO at the beginning to locate the general area around the global minimum then switches to the gradient search method to zero in on it. Examples of the algorithm applied to the detection of interior walls of a building from reflected ultra-wideband radar signals are shown. Other possible applications are optical inspection of coatings and ultrasonic measurement of multilayer structures.