Since seismic reflection data produce comprehensive subsurface images that reveal geological structures likely to contain minerals, oil, and gas, they are essential for resource exploitation. When paired with well logs, these data enable seismic and petrophysical assessments that lower drilling risks and boost efficiency. Wireline logs from five wells and seismic coverage of the Nasiriyah oil field (Dhi-Qar Governorate), processed with Schlumberger software, were used to characterize the Yamama reservoir. Synthetic seismograms connected reservoir tops to seismic data; however, resistivity and gamma logs helped with correlation and sequence delineation. Wells' correlation described the migration routes of hydrocarbons and reservoir c

This work investigates the effect of earthquakes on the stability of a collective pile subjected to seismic loads in the soil layer. Plaxis 3D 2020 finite element software modeled pile behavior in dry soils with sloping layers. The results showed a remarkable fluctuation between the earthquakes, where the three earthquakes (Halabja, El Centro, and Kobe) and the acceleration peak in the Kobe earthquake had a time of about 11 seconds. Different settlement results were shown, as different values were recorded for the three types of earthquakes. Settlement ratios were increased by increasing the seismic intensity; hence the maximum settlement was observed with the model under the effect of the Kobe earthquake (0.58 g), where

The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

In this current work, Purpose; to clearly the fundamental idea for constructing a design and
investigation of spur gear made of composite material its comes from the combination of (high
speeds, low noise, oil-les running, light weight, high strength, and more load capability)
encountered in modern engineering applications of the gear drives, when the usual metallic gear
cannot too overwhelming these combinations.
An analyzing of stresses and deformation under static and dynamic loading for spur gear tooth
by finite element method with isoparametric eight-nodded in total of 200 brick element with 340
nods in three degree of freedom per node was selected for this analysis. This is responsible for the
catastropic fa