Heating, current drive and confinement regimes with the JET ICRH and lhcd systems

During its 1990 operation, 2 large RF systems were available on JET. The Ion Cyclotron Resonance Heating (ICRH) system was equipped with new beryllium screens and with feedback matching systems. Specific impurities generated by ICRH were reduced to negligible levels even in the most stringent H-mode conditions. A maximum power of 22 MW was coupled to L-mode plasmas. High quality H-modes (tau-E greater-than-or-equal-to 2.5 tau-EG) were achieved using dipole phasing. A new high confinement mode was discovered. It combines the properties of the H-mode regime to the low central diffusivities obtained by pellet injection. A value of n(d) tau-E T(i) = 7.8 x 10(20) m-3 s keV was obtained in this mode with T(e) approximately T(i) approximately 11 keV. In the L-mode regime, a regime, a record (140 kW) D-He-3 fusion power was generated with 10 - 14 MW of ICRH at the He-3 cyclotron frequency. Experiments were performed with the prototype launcher of the Lower Hybrid Current Drive (LHCD) systems with coupled power up to 1.6 MW with current drive efficiencies up to < n(e) > R I(CD)/P = 0.4 x 10(20) m-2 A/W. Fast electrons are driven by LHCD to tail temperatures of 100 keV with a hollow radial profile. Paradoxically, LHCD induces central heating particularly in combination with ICRH. Finally we present the first observations of the synergistic acceleration of fast electrons by Transit Time Magnetic Pumping (TTMP) (from ICRH) and Electron Landau Damping (ELD) (from LHCD). The synergism generates TTMP current drive even without phasing the ICRH antennae.

Determining of the delay time for a heating ventilating and air-conditioning plant using two weighted neural network approach

This paper presents an two weighted neural network approach to determine the delay time for a heating, ventilating and air-conditioning (HVAC) plan to respond to control actions. The two weighted neural network is a fully connected four-layer network. An acceleration technique was used to improve the General Delta Rule for the learning process. Experimental data for heating and cooling modes were used with both the two weighted neural network and a traditional mathematical method to determine the delay time. The results show that two weighted neural networks can be used effectively determining the delay time for AVAC systems.

Determining of the delay time for a heating ventilating and air-conditioning plant using multi-weights neurons approach

This paper presents an multi weights neurons approach to determine the delay time for a Heating ventilating and air-conditioning (HVAC) plan to respond to control actions. The multi weights neurons is a fully connected four-layer network. An acceleration technique was used to improve the general delta rule for the learning process. Experimental data for heating and cooling modes were used with both the multi weights neurons and a traditional mathematical method to determine the delay time. The results show that multi weights neurons can be used effectively determining the delay time for HVAC systems.