We have measured the shock-compression parameters for UO/sub 2/ to 330 GPa. The Hugoniot elastic limit was found to be 5.7 GPa. Evidence for a shock-induced phase transition was observed at about 54 GPa.

The Lawrence Livermore Laboratory is involved in a program to produce energy by the laser-induced fusion of microtargets. These laser systems have become very large and powerful. In Shiva, the amplifier rods common to smaller systems have been replaced with an array of large elliptical amplifier discs with major axes as large as 42 cm. Under the high power levels of these devices it is imperative that the optical surfaces remain scrupulously clean. All units are therefore cleaned, assembled, and operated under clean room conditions. In spite of such precautions, persistent problems arise. These can be grouped into three categories: (1) the appearance of unknown contaminants from unknown sources, (2) the transfer of material from one part of the assembly to another, and (3) laser beam damage to the optical surfaces. Theses problems and their resolution by surface analytical methods are discussed. In addition some unique problems associated with the very large sample sizes are described.

This paper reviews some of the computational tools for the assessment and design of Material Control and Accounting (MC and A) process monitoring components and illustrates their application to a Pu evaporator/concentrator unit operation. The codes include: (1) a general-purpose dynamic simulator for modeling the physical phenomenology of various chemical unit operations and their associated measurement systems, (2) an estimation code for simulating the operation of some modern signal processing algorithms (Kalman filter formulation), and (3) a set of detection algorithms for simulating on-line material loss detection algorithms for simulating on-line material loss detection. These codes can be used to address the issues of on-line material accounting and diversion detection for safeguarding SNM, and specifically with respect to arriving at meaningful performance measures. They can be used to compare state-of-the-art with state-of-the-practice and to study cost benefit tradeoffs. They are capable of treating stochastic models with nonlinear process and measurement dynamics and as a result should provide means for better designs of MC and A process monitoring components.