Performance of a 14.9-kW laminated-frame dc series motor with chopper controller

Traction motor using two types of excitation: ripple free dc from a motor generator set for baseline data and chopped dc as supplied by a battery and chopper controller was tested. For the same average values of input voltage and current, the power output was independent of the type of excitation. At the same speeds, motor efficiency at low power output (corresponding to low duty cycle of the controller) was 5 to 10 percentage points less on chopped dc than on ripple-free dc. This illustrates that for chopped waveforms, it is incorrect to calculate input power as the product of average voltage and average current. Locked-rotor torque, no load losses, and magnetic saturation data were so determined

Redistribution of the inlet temperature profile through the SSME fuel turbine

A three-dimensional Euler code was used to predict radial inlet temperature profile redistribution through the two-stage fuel turbopump turbine. The calculation was made at the FPL condition using a turbine inlet radial temperature profile. This same calculation was made earlier on single-stage turbine. There was a redistribution of the temperature profile such that the hotter gas that originated at the midspan region at the turbine inlet was shifted to the hub and tip regions on the blade pressure surface at the rotor exit. For the SSME fuel turbine, however, there was no redistribution of the inlet temperature profile. No strong secondary flow patterns were identified. It is indicated that this trend is attributed to the high solidity SSME blading

Energy consumption and cooperation for optimal sensing

The reliable detection of environmental molecules in the presence of noise is an important cellular function, yet the underlying computational mechanisms are not well understood. We introduce a model of two interacting sensors which allows for the principled exploration of signal statistics, cooperation strategies and the role of energy consumption in optimal sensing, quantified through the mutual information between the signal and the sensors. Here we report that in general the optimal sensing strategy depends both on the noise level and the statistics of the signals. For joint, correlated signals, energy consuming (nonequilibrium), asymmetric couplings result in maximum information gain in the low-noise, high-signal-correlation limit. Surprisingly we also find that energy consumption is not always required for optimal sensing. We generalise our model to incorporate time integration of the sensor state by a population of readout molecules, and demonstrate that sensor interaction and energy consumption remain important for optimal sensing.Comment: 9 pages, 5 figures, Forthcoming in Nature Communication