Thermodynamic Properties of the Dimerised and Frustrated S=1/2 Chain

By high temperature series expansion, exact diagonalisation and temperature density-matrix renormalisation the magnetic susceptibility $\chi(T)$ and the specific heat $C(T)$ of dimerised and frustrated $S=1/2$ chains are computed. All three methods yield reliable results, in particular for not too small temperatures or not too small gaps. The series expansion results are provided in the form of polynomials allowing very fast and convenient fits in data analysis using algebraic programmes. We discuss the difficulty to extract more than two coupling constants from the temperature dependence of $\chi(T)$.Comment: 14 pages, 13 figures, 4 table

Revival of the spin-Peierls transition in Cu_xZn_(1-x)GeO_3 under pressure

Pressure and temperature dependent susceptibility and Raman scattering experiments on single crystalline Cu_xZn_(1-x)GeO_3 have shown an unusually strong increase of the spin-Peierls phase transition temperature upon applying hydrostatic pressure. The large positive pressure coefficient (7.5 K/GPa) - almost twice as large as for the pure compound (4.5 K/GPa) - is interpreted as arising due to an increasing magnetic frustration which decreases the spin-spin correlation length, and thereby weakens the influence of the non-magnetic Zn-substitution.Comment: LaTeX, 15 pages, 5 eps figures, Phys. Rev. B, to appea

Use of expert systems in nuclear power plants

The application of technologies, particularly expert systems, to the control room activities in a nuclear power plant has the potential to reduce operator error and increase plant safety, reliability, and efficiency. Furthermore, there are a large number of nonoperating activities (testing, routine maintenance, outage planning, equipment diagnostics, and fuel management) in which expert systems can increase the efficiency and effectiveness of overall plant and corporate operations. This document presents a number of potential applications of expert systems in the nuclear power field. 36 refs., 2 tabs

Use of artificial intelligence to enhance the safety of nuclear power plants

In the operation of a nuclear power plant, the sheer magnitude of the number of process parameters and systems interactions poses difficulties for the operators, particularly during abnormal or emergency situations. Recovery from an upset situation depends upon the facility with which the available raw data can be converted into and assimilated as meaningful knowledge. Plant personnel are sometimes affected by stress and emotion, which may have varying degrees of influence on their performance. Expert systems can take some of the uncertainty and guesswork out of their decisions by providing expert advice and rapid access to a large information base. Application of artificial intelligence technologies, particularly expert systems, to control room activities in a nuclear power plant has the potential to reduce operator error and improve power plant safety and reliability. 12 refs

Use of probabilistic risk assessment (PRA) in expert systems to advise nuclear plant operators and managers

The use of expert systems in nuclear power plants to provide advice to managers, supervisors and/or operators is a concept that is rapidly gaining acceptance. Generally, expert systems rely on the expertise of human experts or knowledge that has been modified in publications, books, or regulations to provide advice under a wide variety of conditions. In this work, a probabilistic risk assessment (PRA)/sup 3/ of a nuclear power plant performed previously is used to assess the safety status of nuclear power plants and to make recommendations to the plant personnel. 5 refs., 1 fig., 2 tabs

Predicting the severity of nuclear power plant transients by using genetic and nearest neighbor algorithms

Nuclear power plant status is monitored by a human operator. To enhance the operator`s capability to diagnose the nuclear power plant status in case of a transient, several systems were developed to identify the type of the transient. Few of them addressed the further question: how severe is the transient? In this paper, we explore the possibility of predicting the severity of a transient using genetic algorithms and nearest neighbor algorithms after its type has been identified