Taylor relaxation and lambda decay of unbounded, freely expanding spheromaks

A magnetized coaxial gun is discharged into a much larger vacuum chamber and the subsequent evolution of the plasma is observed using high speed cameras and a magnetic probe array. Photographic results indicate four distinct regimes of operation, labeled I–IV, each possessing qualitatively different dynamics, with the parameter lambdagun = µ0Igun/Phibias determining the operative regime. Plasmas produced in Regime II are identified as detached spheromak configurations. Images depict a donut-like shape, while magnetic data demonstrate that a closed toroidal flux-surface topology is present. Poloidal flux amplification shows that Taylor relaxation mechanisms are at work. The spatial and temporal variation of plasma lambda= µ0Jphi/Bphi indicate that the spheromak is decaying and expanding in a manner analogous to a self-similar expansion model proposed for interplanetary magnetic clouds. In Regime III, the plasma is unable to detach from the gun due to excess bias flux. Analysis of toroidal and poloidal flux as well as the lambda profile shows that magnetic flux and helicity are confined within the gun for this regime

Effects of CT injector acceleration electrode configuration on tokamak penetration

Through compact toroid (CT) injection experiments on the TEXT-U tokamak (with BT simeq 10 kG and IP simeq 100 kA), it has been shown that the acceleration electrode configuration, particularly in the vicinity of the toroidal field (TF) coils of the tokamak, has a strong effect on penetration performance. In initial experiments, premature stopping of CTs within the injector was seen at anomalously low TF strengths. Two modifications were found to greatly improve performance: (a) removal of a section of the inner electrode and (b) increased diameter of the 'drift tube' (which guides the CT into the tokamak after acceleration). It is proposed that the primary drag mechanism slowing CTs is toroidal flux trapping, which occurs when a CT displaces transverse TF trapped within the flux conserving walls of the acceleration electrodes (or drift tube). For a simple two dimensional (2-D) geometry, a magnetostatic analysis produces a CT kinetic energy requirement of 1/2ρv2 ≥ α(B02/2μ0), with α = 2/(1-a2/R2) a dimensionless number that is dependent on the CT radius a normalized by the drift tube radius R. For a typical CT, this can greatly increase the required energies. A numerical analysis in 3-D confirms the analytical result for long CTs (with length L such that L/a gtrsim 10). In addition to flux trapping, the CT shape is also shown to affect the energy criterion. These findings indicate that a realistic assessment of the kinetic energy required for a CT to penetrate a particular tokamak TF must take into account the interaction of the magnetic field with the electrode walls of the injector

Connecting remote systems for demonstration of automation technologies

An initial estimate of the communications requirements of the Systems Autonomy Demonstration Project (SADP) development and demonstration environments is presented. A proposed network paradigm is developed, and options for network topologies are explored

A genetic-algorithms based evolutionary computational neural network for modelling spatial interaction data

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see Fischer and Gopal 1994). With the view of modelling interactions over geographic space, this paper considers this problem as a global optimization problem and proposes a novel approach that embeds backpropagation learning into the evolutionary paradigm of genetic algorithms. This is accomplished by interweaving a genetic search for finding an optimal CNN topology with gradient-based backpropagation learning for determining the network parameters. Thus, the model builder will be relieved of the burden of identifying appropriate CNN-topologies that will allow a problem to be solved with simple, but powerful learning mechanisms, such as backpropagation of gradient descent errors. The approach has been applied to the family of three inputs, single hidden layer, single output feedforward CNN models using interregional telecommunication traffic data for Austria, to illustrate its performance and to evaluate its robustness.