This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Levera...This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network(SNN) and deep neural network(DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value,applied load, installation type, and soil configuration(loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering,addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.展开更多
The study aimed to improve the water condensation process in a pyramidal solar still through thermal techniques,focusing on Karbala,Iraq’s hot and dry climate.Various cooling methods-including air and thermoelectric ...The study aimed to improve the water condensation process in a pyramidal solar still through thermal techniques,focusing on Karbala,Iraq’s hot and dry climate.Various cooling methods-including air and thermoelectric cooling with water were integrated and tested in a multi-stage pyramid-shaped solar still to enhance condensation on separate glass surfaces.The system uniquely combines two cooling techniques to address the high thermal load resulting from multiple condensation surfaces.Air cooling(2-8 m/s)and water cooling(105-620 W)were evaluated.Air cooling was applied at speeds of 2,4,6,and 8 m/s with corresponding wattages of 80,120,160,and 200 W.Water cooling with thermoelectric and heat sink methods involved wattages of 105,210,315,and 410 W for each condensation glass,with initial solar radiation intensity measured at 995 W/m^(2)on 24 May 2024.Air cooling increased condensation speed by up to 12%at noon,aided by the dry environment.Water temperature in the basin without cooling reached 65℃,dropping to 53.3℃with a maximum 620 W cooling power consumption.Productivity analysis showed a 48.3%improvement in the morning at an input power of 330 W,which increased to 55%at 620 W.The system achieved a maximum water productivity of 2797 mL/m^(2),with an estimated production cost of 0.078 USD per liter.However,increased energy consumption for cooling reduced overall thermal efficiency due to larger condensation areas in the pyramid solar still requiring more energy,despite enhancing water productivity.展开更多
In terms of battery design and evaluation,Electric Vehicles(EVs)are receiving a great deal of attention as a modern,eco-friendly,sustainable transportation method.In this paper,a novel battery pack is designed to main...In terms of battery design and evaluation,Electric Vehicles(EVs)are receiving a great deal of attention as a modern,eco-friendly,sustainable transportation method.In this paper,a novel battery pack is designed to maintain a uniform temperature distribution,allowing the battery to operate within its optimal temperature range.The proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is maintained.First,a 3-D model of a battery cell was created,followed by thermal simulation for 15C,25C,and 35C ambient temperatures.The simulation results reveal that the temperature distribution is nearly uniform,with slightly higher values in the middle portion of the cell height.Second,using finite element analysis(FEA),it was determined that the heat flux per unit area is nearly uniform with a slight increase at the edges.Third,a machine learning model is proposed by utilizing a neural network(NN).Lastly,the heat flux values were predicted using the NN model that was proposed.The model was assessed based on statistical measures where a root mean square error(RMSE)value of 0.87%was achieved.The NN outperformed FEA in terms of time consumption with a high prediction accuracy,leveraging the potential of adopting machine learning over FEA in related operational assessments.展开更多
文摘This study investigates the load-bearing capacity of open-ended pipe piles in sandy soil, with a specific focus on the impact of soil plug constraints at four levels(no plug, 25% plug, 50% plug, and full plug). Leveraging a dataset comprising open-ended pipe piles with varying geometrical and geotechnical properties, this research employs shallow neural network(SNN) and deep neural network(DNN) models to predict plugging conditions for both driven and pressed installation types. This paper underscores the importance of key parameters such as the settlement value,applied load, installation type, and soil configuration(loose, medium, and dense) in accurately predicting pile settlement. These findings offer valuable insights for optimizing pile design and construction in geotechnical engineering,addressing a longstanding challenge in the field. The study demonstrates the potential of the SNN and DNN models in precisely identifying plugging conditions before pile driving, with the SNN achieving R2 values ranging from0.444 to 0.711 and RMSPE values ranging from 24.621% to 48.663%, whereas the DNN exhibits superior performance, with R2 values ranging from 0.815 to 0.942 and RMSPE values ranging from 4.419% to 10.325%. These results have significant implications for enhancing construction practices and reducing uncertainties associated with pile foundation projects in addition to leveraging artificial intelligence tools to avoid long experimental procedures.
文摘The study aimed to improve the water condensation process in a pyramidal solar still through thermal techniques,focusing on Karbala,Iraq’s hot and dry climate.Various cooling methods-including air and thermoelectric cooling with water were integrated and tested in a multi-stage pyramid-shaped solar still to enhance condensation on separate glass surfaces.The system uniquely combines two cooling techniques to address the high thermal load resulting from multiple condensation surfaces.Air cooling(2-8 m/s)and water cooling(105-620 W)were evaluated.Air cooling was applied at speeds of 2,4,6,and 8 m/s with corresponding wattages of 80,120,160,and 200 W.Water cooling with thermoelectric and heat sink methods involved wattages of 105,210,315,and 410 W for each condensation glass,with initial solar radiation intensity measured at 995 W/m^(2)on 24 May 2024.Air cooling increased condensation speed by up to 12%at noon,aided by the dry environment.Water temperature in the basin without cooling reached 65℃,dropping to 53.3℃with a maximum 620 W cooling power consumption.Productivity analysis showed a 48.3%improvement in the morning at an input power of 330 W,which increased to 55%at 620 W.The system achieved a maximum water productivity of 2797 mL/m^(2),with an estimated production cost of 0.078 USD per liter.However,increased energy consumption for cooling reduced overall thermal efficiency due to larger condensation areas in the pyramid solar still requiring more energy,despite enhancing water productivity.
文摘In terms of battery design and evaluation,Electric Vehicles(EVs)are receiving a great deal of attention as a modern,eco-friendly,sustainable transportation method.In this paper,a novel battery pack is designed to maintain a uniform temperature distribution,allowing the battery to operate within its optimal temperature range.The proposed battery design is part of a main channel where a portion of cool air will pass from an inlet then exit from an outlet where a uniform temperature distribution is maintained.First,a 3-D model of a battery cell was created,followed by thermal simulation for 15C,25C,and 35C ambient temperatures.The simulation results reveal that the temperature distribution is nearly uniform,with slightly higher values in the middle portion of the cell height.Second,using finite element analysis(FEA),it was determined that the heat flux per unit area is nearly uniform with a slight increase at the edges.Third,a machine learning model is proposed by utilizing a neural network(NN).Lastly,the heat flux values were predicted using the NN model that was proposed.The model was assessed based on statistical measures where a root mean square error(RMSE)value of 0.87%was achieved.The NN outperformed FEA in terms of time consumption with a high prediction accuracy,leveraging the potential of adopting machine learning over FEA in related operational assessments.
