Optimization of sensor locations for measurement of flue gas flow in industrial ducts and stacks using neural networks

This paper presents a novel application of neural network modeling in the optimization of sensor locations for the measurement of flue gas flow in industrial ducts and stacks. The proposed neural network model has been validated with an experiment based upon a case-study power plant. The results have shown that the optimized sensor location can be easily determined with this model. The industry can directly benefit from the improvement of measurement accuracy of the flue gas flow in the optimized sensor location and the reduction of manual measurement operation with Pitot tube

Experimental investigation of piloted flameholders

Four configurations of piloted flameholders were tested. The range of flame stabilization, flame propagation, pressure oscillation during ignition, and pressure drop of the configurations were determined. Some tests showed a very strong effect of inlet flow velocity profile and flameholder geometry on flame stabilization. These tests led to the following conclusions. (1) The use of a piloted flameholder in the turbofan augmentor may minimize the peak pressure rise during ignition. At the present experimental conditions, delta P/P asterisk over 2 is less than 10 percent; therefore, the use of a piloted flameholder is a good method to realize soft ignition. (2) The geometry of the piloted flameholder and the amount of fuel injected into the flameholder have a strong effect on the pressure oscillation during ignition of the fuel-air mixture in the secondary zone. (3) Compared with the V-gutter flameholder with holes in its wall, the V-gutter flameholder without holes not only has advantages such as simple structure and good rigidity but offers a wide combustion stability limit and a high capability of igniting the fuel-air mixture of the secondary zone

Integration of crosswind forces into train dynamic modelling

In this paper a new method is used to calculate unsteady wind loadings acting on a railway vehicle. The method takes input data from wind tunnel testing or from computational fluid dynamics simulations (one example of each is presented in this article), for aerodynamic force and moment coefficients and combines these with fluctuating wind velocity time histories and train speed to produce wind force time histories on the train. This method is fast and efficient and this has allowed the wind forces to be applied to a vehicle dynamics simulation for a long length of track. Two typical vehicles (one passenger, one freight) have been modelled using the vehicle dynamics simulation package ‘VAMPIRE®’, which allows detailed modelling of the vehicle suspension and wheel—rail contact. The aerodynamic coefficients of the passenger train have been obtained from wind tunnel tests while those of the freight train have been obtained through fluid dynamic computations using large-eddy simulation. Wind loadings were calculated for the same vehicles for a range of average wind speeds and applied to the vehicle models using a user routine within the VAMPIRE package. Track irregularities measured by a track recording coach for a 40 km section of the main line route from London to King's Lynn were used as input to the vehicle simulations. The simulated vehicle behaviour was assessed against two key indicators for derailment; the Y/Q ratio, which is an indicator of wheel climb derailment, and the Δ Q/Q value, which indicates wheel unloading and therefore potential roll over. The results show that vehicle derailment by either indicator is not predicted for either vehicle for any mean wind speed up to 20 m/s (with consequent gusts up to around 30 m/s). At a higher mean wind speed of 25 m/s derailment is predicted for the passenger vehicle and the unladen freight vehicle (but not for the laden freight vehicle)