Hal R. Brand

Measurements of reduced corkscrew motion on the ETA-II linear induction accelerator

The ETA-II linear induction accelerator is used to drive a microwave free-electron laser (FEL). Corkscrew motion, which previously limited performance, has been reduced by: (1) an improved pulse distribution system which reduces energy sweep, (2) improved magnetic alignment achieved with a stretched wire alignment technique (SWAT), and (3) a unique magnetic tuning algorithm. Experiments have been carried out on a 20-cell version of ETA-II operating at 1500 A and 2.7 MeV. The measured transverse beam motion is less than 0.5 mm for 40 ns of the pulse, an improvement of a factor of 2 to 3 over previous results. Details of the computerized tuning procedure, estimates of the corkscrew phase, and relevance of these results to future FEL experiments are presented.<<ETX>>

Development of a totally computer-controlled triple quadrupole mass spectrometer system

A totally computer‐controlled triple quadrupole mass spectrometer (TQMS) is described. It has a number of unique features not available on current commercial instruments, including: complete computer control of source and all ion axial potentials; use of dual computers for data acquisition and data processing; and capability for self‐adaptive control of experiments. Furthermore, it has been possible to produce this instrument at a cost significantly below that of commercial instruments. This triple quadrupole mass spectrometer has been constructed using components commercially available from several different manufacturers. The source is a standard Hewlett‐Packard 5985B GC/MS source. The two quadrupole analyzers and the quadrupole CAD region contain Balzers QMA 150 rods with Balzers QMG 511 rf controllers for the analyzers and a Balzers QHS‐511 controller for the CAD region. The pulsed‐positive‐ion‐negative‐ion‐chemical ionization (PPINICI) detector is made by Finnigan Corporation. The mechanical and elec...

Artificial intelligence techniques for tuning linear induction accelerators

An expert system has been developed that acts as an intelligent assistant for tuning particle beam accelerators. This system is called MAESTRO-Model and Expert System Tuning Resource for Operators. MAESTRO maintains a knowledge base of the accelerator containing not only the interconnections of the beamline components, but also their physical attributes such as measured magnetic tilts, offsets, and field profiles. MAESTRO incorporates particle trajectory and beam envelope models which are coupled to the knowledge base permitting large number of real-time orbit and envelope calculations in the control-room environment. This capability has been used (1) to implement a tuning algorithm for minimizing transverse beam motion, (2) to produce a beam waist with arbitrary radius at the entrance to a brightness diagnostic, and (3) to measure beam energy along the accelerator by fitting orbits to focusing and steering sweeps.<<ETX>>

Characterization of the flow dynamics of an enzyme reaction system

Abstract The characterization of the flow dynamics of an automated enzyme reaction system is discussed. Parametric and non-parametric characterizations are constructed and then tested against actual experiments. The analytical model provides a more compact representation than the numerical model. A second-order lag-plus-dead-time model gives a good fit to the experimental data.

MAESTRO — A model and expert system tuning resource for operators

Abstract We have developed MAESTRO, a m odel a nd e xpert s ystem t uning r esource for o perators. It provides a unified software environment for optimizing the performance of large, complex machines, in particular the Advanced Test Accelerator and Experimental Test Accelerator at Lawrence Livermore National Laboratory. The system incorporates three approaches to tuning: • - a mouse-based manual interface to select and control magnets and to view displays of machine performance; • - an automation based on “cloning the operator” by implementing the strategies and reasoning used by the operator; and • - an automation based on a simulator model which, when accurately matched to the machine, allows downloading of optimal sets of parameters and permits diagnosing errors in the beam line. The latter two approaches are based on the artificial-intelligence technique known as Expert Systems.

Characterization of Static- and Fatigue-Loaded Carbon Composites by X-Ray CT

The development and improvement of advanced materials is strictly connected to the understanding of the properties and behavior of such materials as a function of both their macro and micro-structures. The application of X-ray computed tomography (CT) to these materials allows for a better understanding of the materials properties and behavior on either macro or micro-structure scales. The authors applied CT to study a set of aerospace grade carbon fiber/thermoplastic matrix composites. Samples of APC-2 (PEEK/AS4) were subjected to either static or high-stress fatigue loading in tension. Both notched (central circular hole) and unnotched specimens were examined. They are investigating a high-temperature thermoplastic polyimide composite sample by acquiring CT data sets before, during (at set intervals), and after full-reversal (tension-compression), low-stress fatigue loading at the upper use temperature. The CT scanner employed and the results obtained in the analysis of 3D CT data sets to study the defects and other features within the different composites are presented in this report.

LIAM-a linear induction accelerator model

A flexible linear induction accelerator model (LIAM) was developed to predict both beam centroid position and the beam envelope. LIAM requires on-axis magnetic profiles and is designed to easily handle overlapping fields from multiple elements. Currently, LIAM includes solenoids, dipole steering magnets, and accelerating gaps. Other magnetic elements can be easily incorporated into LIAM due to its object-oriented design. LIAM is written in the C programming language and computes fast enough on current workstations to be used in the control room as a tuning and diagnostic aid. Combined with a non-linear least squares package, LIAM has been used to estimate beam energy at various locations within the ETA-II accelerator.<<ETX>>

AN EXPERT SYSTEM FOR TUNING PARTICLE BEAM ACCELERATORS

An expert system that acts as an intelligent assistant to operators tuning a particle beam accelerator was developed. The system incorporates three approaches to tuning: (1) Duplicating within a software program the reasoning and the procedures used by an operator to tune an accelerator. This approach has been used to steer particle beams through the transport section of Lawrence Livermore National Laboratorys Advanced Test Accelerator and through the injector section of the Experimental Test Accelerator. (2) Using a model to simulate the position of a beam in an accelerator. The simulation is based on data taken directly from the accelerator while it is running. This approach will ultimately be used by operators of the Experimental Test Accelerator to first compare actual and simulated beam performance in real time, then to determine which set of parameters is optimum in terms of centering the beam, and finally to feed those parameters to the accelerator. Operators can also use the model to determine if a component has failed. (3) Using a mouse to manually select and control the magnets that steer the beam. Operators on the Experimental Test Accelerator can also use the mouse to call up windows that display the horizontal and vertical positions of the beam as well as its current.

An Expert System For Tuning Particle-Beam Accelerators

We have developed a proof-of-concept prototype of an expert system for tuning particle beam accelerators. It is designed to function as an intelligent assistant for an operator. In its present form it implements the strategies and reasoning followed by the operator for steering through the beam transport section of the Advanced Test Accelerator at Lawrence Livermore Laboratorys Site 300. The system is implemented in the language LISP using the Artificial Intelligence concepts of frames, daemons, and a representation we developed called a Monitored Decision Script.

Development of an AI-Based Optimization System for Tandem Mass Spectrometry

Artificial intelligence (AI) is that branch of computer science that attempts to understand and model intelligent behavior with the aid of computers. In general these attempts to have machines emulate intelligent behavior fall far short of the competence of humans. However, in the area of expert systems, computer programs have been developed that can achieve human performance, and in limited aspects even exceed it.