Swam Intelligent Based Inverse Model for Laboratory Double Reservoir Diffusion Experiments

Theoretical approaches are of fundamental importance to predict the potential impact of waste disposal facilities on ground water contamination. Appropriate design parameters are, in general, estimated by fitting the theoretical models to a field monitoring or laboratory experimental data. Double-reservoir diffusion (Transient Through-Diffusion) experiments are generally conducted in the laboratory to estimate the mass transport parameters of the proposed barrier material. These design parameters are estimated by manual parameter adjusting techniques (also called eye-fitting) like Pollute. In this work an automated inverse model is developed to estimate the mass transport parameters from transient through-diffusion experimental data. The proposed inverse model uses particle swarm optimization (PSO) algorithm which is based on the social behaviour of animals for finding their food sources. Finite difference numerical solution of the transient through-diffusion mathematical model is integrated with the PSO algorithm to solve the inverse problem of parameter estimation.The working principle of the new solver is demonstrated by estimating mass transport parameters from the published transient through-diffusion experimental data. The estimated values are compared with the values obtained by existing procedure. The present technique is robust and efficient. The mass transport parameters are obtained with a very good precision in less time

An intelligent procurement marketplace for web services composition

This paper presents an intelligent procurement marketplace for finding the best mix of web services to dynamically compose the business process desired by a web service requester. We develop a combinatorial auction approach that leads to an integer programming formulation for the web services composition problem. The model takes into account the Quality of Service (QoS) and Service Level Agreements (SLA) for differentiating among multiple service providers who are capable of fulfilling a functionality. An important feature of the model is interface aware composition.

Root cause diagnostics using temporal data mining

A method, system, and computer program product for fault data correlation in a diagnostic system are provided. The method includes receiving the fault data including a plurality of faults collected over a period of time, and identifying a plurality of episodes within the fault data, where each episode includes a sequence of the faults. The method further includes calculating a frequency of the episodes within the fault data, calculating a correlation confidence of the faults relative to the episodes as a function of the frequency of the episodes, and outputting a report of the faults with the correlation confidence.

Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Structural health monitoring of aerospace structural components using wave propagation based diagnostics

The paper discusses a wave propagation based method for identifying the damages in an aircraft built up structural component such as delamination and skin-stiffener debonding. First, a spectral finite element mode l (SFEM) is developed for modeling wave propagation in general built-up structures by using the concept of assembling 2D spectral plate elements. The developed numerical model is validated using conventional 2-D FEM. Studies are performed to capture the mode coupling,that is, the flexural-axial coupling present in the wave responses. Lastly, the damages in these built up structures are then identified using the developed SFEM model and the measured responses using the concept Damage Force Indicator (DFI) technique.

Intelligent prediction of ground vibration in coal formations using ANN

The present paper details the prediction of blast induced ground vibration, using artificial neural network. The data was generated from five different coal mines. Twenty one different parameters involving rock mass parameters, explosive parameters and blast design parameters, were used to develop the one comprehensive ANN model for five different coal bearing formations. A total of 131 datasets was used to develop the ANN model and 44 datasets was used to test the model. The developed ANN model was compared with the USBM model. The prediction capability to predict blast induced ground vibration, of the comprehensive ANN model was found to be superior.

In-cylinder investigations and analysis of a SI gas engine fuelled with H-2 and CO rich syngas fuel: Sensitivity analysis of combustion descriptors for engine diagnostics and control

The sensitivity of combustion phasing and combustion descriptors to ignition timing, load and mixture quality on fuelling a multi-cylinder natural gas engine with bio-derived H-2 and CO rich syngas is addressed. While the descriptors for conventional fuels are well established and are in use for closed loop engine control, presence of H-2 in syngas potentially alters the mixture properties and hence combustion phasing, necessitating the current study. The ability of the descriptors to predict abnormal combustion, hitherto missing in the literature, is also addressed. Results from experiments using multi-cylinder engines and numerical studies using zero dimensional Wiebe function based simulation models are reported. For syngas with 20% H-2 and CO and 2% CH4 (producer gas), an ignition retard of 5 +/- 1 degrees was required compared to natural gas ignition timing to achieve peak load of 72.8 kWe. It is found that, for syngas, whose flammability limits are 0.42-1.93, the optimal engine operation was at an equivalence ratio of 1.12. The same methodology is extended to a two cylinder engine towards addressing the influence of syngas composition, especially H-2 fraction (varying from 13% to 37%), on the combustion phasing. The study confirms the utility of pressure trace derived combustion descriptors, except for the pressure trace first derivative, in describing the MBT operating condition of the engine when fuelled with an alternative fuel. Both experiments and analysis suggest most of the combustion descriptors to be independent of the engine load and mixture quality. A near linear relationship with ignition angle is observed. The general trend(s) of the combustion descriptors for syngas fuelled operation are similar to those of conventional fuels; the differences in sensitivity of the descriptors for syngas fuelled engine operation requires re-calibration of control logic for MBT conditions. Copyright (C) 2014, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.

Optical diagnostics of fuel-air mixing and vortex formation in a cavity combustor

The current work reports optical diagnostic measurements of fuel-air mixing and vortex structure in a single cavity trapped vortex combustor (TVC). Specifically, the mixture fraction using acetone PLIF technique in the non-reacting flow, and PIV measurements in the reacting flow are reported for the first time in trapped vortex combustors. The fuel-air momentum flux ratio, where the air momentum corresponds to that entering the cavity through a specially-incorporated flow guide vane, is used to characterize the mixing. The acetone PLIF experiments show that at high momentum flux ratios, the fuel-air mixing in the cavity is very minimal and is enhanced as the momentum flux ratio reduces, due to a favourable vortex formation in the cavity. Stoichiometric mixture fraction surfaces show that the mixing causes the reaction surfaces to shift from non-premixed to partially-premixed stratified mixtures. PIV measurements conducted in the non-reacting flow in the cavity further reinforce this observation. The scalar dissipation rates of mixture fraction were compared with the contours of RMS of fluctuating velocity and showed very good agreement. The regions of maximum mixing are observed to be along the fuel air interface. Reacting flow Ply measurements which differ substantially from the non-reacting cases primarily because of the heat release from combustion and the resulting gas expansion show that the vortex is displaced from the centre of the cavity towards the guide vane. Overall, the measurements show interesting features of the flow including the presence of the dual cavity structure and lead to a clear understanding of the underlying physics of the cavity flow highlighting the importance of the fuel-air momentum ratio parameter. (C) 2014 Elsevier Inc. All rights reserved.

QUANTITATIVE OH PLANAR LASER INDUCED FLUORESCENCE DIAGNOSTICS OF SYNGAS AND METHANE COMBUSTION IN A CAVITY COMBUSTOR

The current work reports quantitative OH species concentration in the cavity of a trapped vortex combustor (TVC) in the context of mixing and flame stabilization studies using both syngas and methane fuels. Planar laser induced fluorescence (PLIF) measurements of OH radical obtained using a Nd: YAG pumped dye laser are quantified using a flat flame McKenna burner. The momentum flux ratio (MFR), defined as the ratio of the cavity fuel jet momentum to that of the guide vane air stream, is observed to be a key governing parameter. At high MFRs similar to 4.5, the flame front is observed to form at the interface of the fuel jet and the air jet stream. This is substantiated by velocity vector field measurements. For syngas, as the MFR is lowered to similar to 0.3, the fuel-air mixing increases and a flame front is formed at the bottom and downstream edge of the cavity where a stratified charge is present. This trend is observed for different velocities at similar equivalence ratios. In case of methane combustion in the cavity, where the MFRs employed are extremely low at similar to 0.01, a different mechanism is observed. A fuel-rich mixture is now observed at the center of the cavity and this mixture undergoes combustion. On further increase of the cavity equivalence ratio, the rich mixture exceeds the flammability limit and forms a thin reaction zone at the interface with air stream. As a consequence, a shear layer flame at the top of the cavity interface with the mainstream is also observed. The equivalence ratio in the cavity also determines the combustion characteristics in the case of fuel-air mixtures that are formed as a result of the mixing. Overall, flame stabilization mechanisms have been proposed, which account for the wide range of MFRs and premixing in the mainstream as well.