Shatt Al-Arab University College is a private Iraqi university established in 1993 in Basrah in the south of Iraq..
This study presents a comprehensive integrated geotechnical and geophysical site investigation conducted for the Al-Hilla-2 Gas Power Plant project in Abu-Graq, Babylon Governorate, Iraq. The primary objective was to characterize the subsurface conditions to a depth of 20 m for optimal foundation design. Down-hole seismic surveys revealed a significant and systematic increase in both compressional (P-wave) and shear (S-wave) velocities with depth, with P-wave velocities ranging from 293 to 977 m/s and S-wave velocities from 130 to 408 m/s. These velocities were used to derive key dynamic elastic moduli, including Young's modulus, shear modulus, bulk modulus, Lame's constant and Poisson's ratio, all of which quantitatively confirmed a trend of increasing soil competence and stiffness with depth. Complementary Standard Penetration Test (N-values) increased from 14 to 15 blows/ft in the upper layers to 46 blows/ft at 18 m, classifying the soils as stiff to very stiff clays and dense to very dense sands. The investigation successfully identified a stratigraphic sequence of six distinct geological layers and precisely located the groundwater table at 2.5 m depth. Based on the calculated average shear wave velocity (Vs30) of approximately 260-280 m/s, the site is classified as NEHRP Class D, borderline to Class D, indicating stiff soil conditions suitable for heavy industrial foundations. The results provide geotechnical parameters and a stratigraphic model required for designing reliable foundation systems, mitigating settlement and shear failure risks.
Hot water and steam are important for many utilities. Flat plat solar water heaters are an efficient means for solar energy consumption. In present work, flat plat solar water heater designed and tested under Basra climate conditions. As well as, a numerical validation and enhancement of the basic model are conducted in this study. Turbulent k-ɛ, model was used to solve the 3D model using ANSYS Fluent 2020 R1. Three models basic, basic model with extended surfaces, enlarging the diameter of the basic model are numerically examined. Experimental results show the highest temperature achieved in storage tank in about 34.4 ℃, 42.7℃ in Jan., Mar. respectively. Numerical results show the higher diameter tubes allowed more surface area exposed to the sunshine so as to, higher outlet temperatures are achieved. Overall, the results demonstrate that system performance strongly depends on both solar intensity and volume flow rate. The optimal operating condition is achieved around 12:30 PM, at which the collector delivers maximum useful energy (≈ 600 W) and highest efficiencies (first law ≈ 70% and second law ≈ 54%).
The sources have a huge potential becoming sustainable sources and one of them is geothermal energy and its decisions making in context of GWP (Global Warming Potential) are of interest. This paper will discuss thermodynamic and exergetic performance of a Ground Source Heat Pump (GSHP) system with a refrigerant R410A which consists of vertical Ground Heat Exchanger (GHE) in the climatic condition of Baghdad. Experimental data were analyzed using Engineering Equation Solver (EES) software and were used to calculate the energy and exergy rates, which could then be utilized to examine the system in detail with respect to the key system components. In addition, an energy and exergy analysis of six low GWP refrigerants were carried out as an alternative to R410A at constant pressure ratio (3000 Kpa/700Kpa) detailed. The results indicated exergetic efficiency of the ground heat pump as 68.14% and 38.42% for overall system confirming high values of irreversibility losses particularly in the compressor and expansion valve. The analysis shows that R32 delivers the highest overall Coefficient of Performance (COP) of all other refrigerants studied at 2.388 and the highest overall exergy efficiency was obtained as 47.52% while exergy destruction was 36% higher than for R410A. R513A features thermodynamic limitation, and has a good exergy performance. The outcomes show thermodynamic consistency over the entire system and may prove beneficial in optimizing the efficiency and sustainability of GSHP.
Portable generators are widely used in Iraq to supply electricity during blackouts. However, they are noisy due to the engine’s combustion chamber and moving parts, which can negatively impact the neuroendocrine, cardiovascular, pulmonary, and digestive systems. In this work, the noise reduction of the generator using an acoustic enclosure made of local materials has been studied experimentally. The enclosure was made of medium-density fiber, galvanized iron, glass wool, cork, air gap, and compressed sponge. A 2-kW generator was tested for sound level in two scenarios: with and without multi-layered enclosure containing shredded plastic. The outcomes included determining the sound level and reduction in noise caused by the generator in decibels during the day and at night for various load conditions ranging from 0 to 25
This study suggests that RF and ANN are proven to be robust algorithms in predicting in-situ soil density, which is considered a significant geotechnical parameter. The research is based on 86 soil samples and focuses on five main input parameters: Gravel Percentage (G%), Plastic Limit (PL%), Sand Percentage (S%), Fines Percentage (F%), and Liquid Limit (LL%). The models developed here utilize five commonly recorded index properties (G%, S%, F%, LL, and PL) for all field samples taken from the Basra-Faw Road project. The influence of moisture content and compressive energy was ignored, as all field samples acquired the same moisture content and compressive energy during compression. The fit of the two models was thoroughly tested with statistical indices, including the coefficient of determination (R2) and Root Mean Square Error (RMSE). The analysis shows that the ANN model has better predictive performance compared to the RF model, with the R2 and the RMSE equal to 0.98786 and 0.0027 for the ANN model and 0.96249 and 0.0192 for the RF model. This result emphasizes the ANN's great capability in capturing the complicated non-linear relationship between input variables and soil density. Moreover, the study reveals gravel and fines percentages as the most significant parameters that control the prediction of soil density. Results indicated that machine learning methods, namely ANN, can be an easy, quick, and nondestructive alternative to traditional field-testing methods to predict soil compaction. The research findings add to the base of the art in geotechnical engineering by highlighting the benefits of advanced predictive tools in improving soil density revocation accuracy and efficiency. Incorporating other factors, such as moisture content and compressive energy during compaction, into future datasets may enhance the model's generalizability and accuracy. The findings of this study can have significant implications for bidding purposes and safety in infrastructure-related design; the accuracy of the soil density predictions is critical to such applications as foundation design, slope protection, and pavement construction.
