@article{MosaviNajafiFaizollahzadehArdabilietal.,
  author    = {Mosavi, Amir and Najafi, Bahman and Faizollahzadeh Ardabili, Sina and Shamshirband, Shahaboddin and Rabczuk, Timon},
  title     = {An Intelligent Artificial Neural Network-Response Surface Methodology Method for Accessing the Optimum Biodiesel and Diesel Fuel Blending Conditions in a Diesel Engine from the Viewpoint of Exergy and Energy Analysis},
  series = {Energies},
  volume    = {2018},
  journal   = {Energies},
  number    = {11, 4},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/en11040860},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20180507-37467},
  pages     = {18},
  abstract  = {Biodiesel, as the main alternative fuel to diesel fuel which is produced from renewable and available resources, improves the engine emissions during combustion in diesel engines. In this study, the biodiesel is produced initially from waste cooking oil (WCO). The fuel samples are applied in a diesel engine and the engine performance has been considered from the viewpoint of exergy and energy approaches. Engine tests are performed at a constant 1500 rpm speed with various loads and fuel samples. The obtained experimental data are also applied to develop an artificial neural network (ANN) model. Response surface methodology (RSM) is employed to optimize the exergy and energy efficiencies. Based on the results of the energy analysis, optimal engine performance is obtained at 80\% of full load in presence of B10 and B20 fuels. However, based on the exergy analysis results, optimal engine performance is obtained at 80\% of full load in presence of B90 and B100 fuels. The optimum values of exergy and energy efficiencies are in the range of 25-30\% of full load, which is the same as the calculated range obtained from mathematical modeling.},
  subject      = {Biodiesel},
  language  = {en}
}
@article{MousaviSteinkeJuniorTeixeiraetal.,
  author    = {Mousavi, Seyed Nasrollah and Steinke J{\´u}nior, Renato and Teixeira, Eder Daniel and Bocchiola, Daniele and Nabipour, Narjes and Mosavi, Amir and Shamshirband, Shahaboddin},
  title     = {Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods},
  series = {Mathematics},
  volume    = {2020},
  journal   = {Mathematics},
  number    = {Volume 8, Issue 3, 323},
  publisher = {MDPI},
  address   = {Basel},
  doi       = {10.3390/math8030323},
  url       = {http://nbn-resolving.de/urn:nbn:de:gbv:wim2-20200402-41140},
  pages     = {16},
  abstract  = {Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k\%), and the statistical coefficient of the probability distribution (Nk\%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk\%. The values of the Nk\% coefficient indicated a single mean value for each probability.},
  subject      = {Maschinelles Lernen},
  language  = {en}
}