This paper presents a comparison of simulation’s results with the experimental data from a series of small-scale tests conducted by Joachim and Lunderman of the United States Army Engineer Waterways Experiment Station. The purpose of the experiments was to evaluate the effect of water as a mean of reducing airblast pressure from accidental explosions in underground magazines. In the present study, a series of three-dimensional numerical calculations were conducted using a Multimaterial Eulerian Finite Element Code. Results from the numerical simulations show good comparison with the experimental data for the case with and without water. Our simulation ascertains the mitigation effects of water in reducing the maximum peak pressure and impulse density due to an explosion.
Building energy assessment is essential to accomplish the sustainable energy targets of new and present buildings. Retrofitting of the existing buildings by assessing them through energy models is the most prominent method. Studies revealed that there is still blank information about the building stocks, and these affect the valuation of building energy efficiency policies. Literature also recommends that the existing energy models are too complex and unreliable to predict the energy use. Reliability of such energy models would improve through a better alignment of the input parameters and the model assumptions. The authors hypothesized that the reliability of models would be improved through identification of the most relevant energy use parameters for the building stocks in different regions and models. One of the most commonly accepted methods for detecting the most dominant input parameters is sensitivity analysis, though its shortcomings include the need for a large number of data samples and long computing time. In this research, the Energy, Carbon, and Cost Assessment for Buildings Stocks (ECCABS) model is adopted to identify the most important parameters of the presented model. The research team uses the model that has been validated by studies conducted for the UK building stock. Moreover, by assessing the feasibility study with the stepwise regression to identify the significant input parameters have been discussed. Results show that stepwise regression method could produce the same results compared to sensitivity analysis. This paper also indicates that stepwise regression is considerably faster and less computationally intensive compared to common sensitivity analysis methods.
Many existing system modeling techniques based on statistical modeling, data mining and machine learning have a shortcoming of building variable relations for the full ranges of variable values using one model, although certain variable relations may hold for only some but not all variable values. This shortcoming is overcome by the Partial-Value Association Discovery (PVAD) algorithm that is a new multivariate analysis algorithm to learn both full-value and partial-value relations of system variables from system data. Our research used the PVAD algorithm to model variable relations of energy consumption from data by learning full-and partial-value variable relations of energy consumption. The PVAD algorithm was applied to data of energy consumption obtained from a building at Arizona State University (ASU). Full-and partial-value variable associations of building energy consumption from the PVAD algorithm are compared with variable relations from a decision tree algorithm applied to the same data to show advantages of the PVAD algorithm in modeling the energy consumption system.
The water mitigation effects on the detonations of explosive in a confined cube chamber are studied by use of a numerical code. Significant mitigation effects on shock pressure at various loading densities, various water/explosive mass ratios and various air gaps are obtained. Reduction of final static pressure is also obtained because water absorbs the energy released from the detonations through being compressed and vaporized. On the basis of this study, the detonations in a tunnel system with vent are further simulated. The numerical results are compared with the experimental data, and the water mitigation effects on shock pressure are verified.
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