2,763 research outputs found

    Vibration Control

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    Depth estimation of inner wall defects by means of infrared thermography

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    There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

    Prediction of blast loading in an internal environment using artificial neural networks

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    Explosive loading in a confined internal environment is highly complex and is driven by nonlinear physical processes associated with reflection and coalescence of multiple shock fronts. Prediction of this loading is not currently feasible using simple tools, and instead specialist computational software or practical testing is required, which are impractical for situations with a wide range of input variables. There is a need to develop a tool which balances the accuracy of experiments or physics-based numerical schemes with the simplicity and low computational cost of an engineering-level predictive approach. Artificial neural networks (ANNs) are formed of a collection of neurons that process information via a series of connections. When fully trained, ANNs are capable of replicating and generalising multi-parameter, high-complexity problems and are able to generate new predictions for unseen problems (within the bounds of the training variables). This article presents the development and rigorous testing of an ANN to predict blast loading in a confined internal environment. The ANN was trained using validated numerical modelling data, and key parameters relating to formulation of the training data and network structure were critically analysed in order to maximise the predictive capability of the network. The developed network was generally able to predict specific impulses to within 10% of the numerical data: 90% of specific impulses in the unseen testing data, and between 81% and 87% of specific impulses for data from four additional unseen test models, were predicted to this accuracy. The network was highly capable of generalising in areas adjacent to reflecting surfaces and as those close to ambient outflow boundaries. It is shown that ANNs are highly suited to modelling blast loading in a confined internal environment, with significant improvements in accuracy achievable if a robust, well distributed training dataset is used with a network structure that is tailored to the problem being solved

    Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches

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    Ground vibrations are a critical factor in the rock blasting process. The instantaneous load application exerted by the gas pressure during the detonation process acts on the blasthole walls creating dynamic stresses in the adjacent rock. This triggers different sorts of stress waves, mainly divided into two categories: body and surface waves. The first comprises the P and the S waves, while the second comprises Rayleigh waves. These waves spread concentrically starting at the blast location and move along the ground surface and its interior, being attenuated as they reach further distances. In most cases, and accepting the hypothesis that the attenuation of the vibrational waves is proportional to the distance and inverse to the energy released during the blast, the vibration from a large blast can be perceived from far away. In any case, the ground vibrations can affect pit slopes’ stability, and they can also damage man-made structures. Therefore, ground vibrations need to be predicted, monitored, and controlled to minimize the vibration-caused disturbance to nearby or far elements. The assessment of vibrations produced by blasting has traditionally relied on maximum charge weight per delay scaling laws. These two-parameter or three-parameter models depend on a curve fit to measured data. In this approach (scaled laws), the ground vibration waveforms are not used in the vibration level estimation, neither are other blast design parameters, such as burden, spacing, hole diameter, explosive density, uniaxial compressive strength of the rock, Young’s modulus, subdrilling, stemming, and charge length, to name a few. To provide a more comprehensive approach to ground vibration modeling, including the aforementioned variables, artificial neural networks (ANN) have been employed in several studies worldwide with promising results. The present thesis uses ANN applied to ground vibration modeling, considering the blasting parameters in the input, unlike the empirical approaches, using data from an open-pit gold mine in La Libertad region, Peru. The results from this study are then compared against the traditional scaled distance approach. Two datasets were used, the first was comprised of 178 shots and the second, 80 shots. The first dataset was collected at the La Arena community, and the second was collected at the La Ramada community. Both of these communities are the most populated in the direct area of influence of the mine. When comparing the measured and predicted PPV values using the scale-distance method in the La Arena community, the coefficient of determination () found was 0.1166, while the found when comparing the measured and predicted PPV values using the optimum trained artificial network was 0.5915. Following the same comparison, the value found in the La Ramada community was 0.1035 using the scaled distance method, and the found using the optimum trained artificial network was 0.5139

    Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

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    Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG

    A critical review of a computational fluid dynamics (CFD)-based explosion numerical analysis of offshore facilities

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    In oil and gas industries, the explosive hazards receive lots of attention to achieve a safety design of relevant facilities. As a part of the robust design for offshore structures, an explosion risk analysis is normally conducted to examine the potential hazards and the influence of them on structural members in a real explosion situation. Explosion accidents in the oil and gas industries are related to lots of parameters through complex interaction. Hence, lots of research and industrial projects have been carried out to understand physical mechanism of explosion accidents. Computational fluid dynamics-based explosion risk analysis method is frequently used to identify contributing factors and their interactions to understand such accidents. It is an effective method when modelled explosion phenomena including detailed geometrical features. This study presents a detailed review and analysis of Computational Fluid Dynamics-based explosion risk analysis that used in the offshore industries. The underlying issues of this method and current limitation are identified and analysed. This study also reviewed potential preventative measures to eliminate such limitation. Additionally, this study proposes the prospective research topic regarding computational fluid dynamics-based explosion risk analysis

    Blasting Vibration Monitoring and a New Vibration Reduction Measure

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    Vibration waves generated by blasting can cause shock to buildings. Different responses occur in different parts of the building. Therefore, a single standard is inaccurate. At the same time, methods to reduce vibration are needed. In this paper, the variation of peak particle velocity (PPV) and principal frequency was analyzed. The energy variation of blast vibration waves was analyzed by wavelet packet decomposition. A numerical model was established to verify the new vibration reduction measure. The results showed that the PPV on the walls increases with their height. The PPV and principal frequency of different structures of single-story brick-concrete buildings are different. The amplification factor of PPV does not change much when the principal frequency ratio is larger than 0.75. Measuring points at different heights have different sensitivities to blasting vibration waves of different principal frequencies. Therefore, different structures will respond differently to the same blasting operation. The PPV can be reduced by waveform interference. However, the cycle of blasting vibration waves decreases with increasing distance. Therefore, it is necessary to determine a reasonable interval to reduce the PPV. This requires further research

    Blasting Vibration Monitoring and a New Vibration Reduction Measure

    Get PDF
    Vibration waves generated by blasting can cause shock to buildings. Different responses occur in different parts of the building. Therefore, a single standard is inaccurate. At the same time, methods to reduce vibration are needed. In this paper, the variation of peak particle velocity (PPV) and principal frequency was analyzed. The energy variation of blast vibration waves was analyzed by wavelet packet decomposition. A numerical model was established to verify the new vibration reduction measure. The results showed that the PPV on the walls increases with their height. The PPV and principal frequency of different structures of single-story brick-concrete buildings are different. The amplification factor of PPV does not change much when the principal frequency ratio is larger than 0.75. Measuring points at different heights have different sensitivities to blasting vibration waves of different principal frequencies. Therefore, different structures will respond differently to the same blasting operation. The PPV can be reduced by waveform interference. However, the cycle of blasting vibration waves decreases with increasing distance. Therefore, it is necessary to determine a reasonable interval to reduce the PPV. This requires further research
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