This study was conducted to investigate the effect of diesel fuels on in-cylinder pressure, engine performance and exhaust emissions. In this study, common-rail diesel engine was used with a cylinder volume of 1,500 c...
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This study was conducted to investigate the effect of diesel fuels on in-cylinder pressure, engine performance and exhaust emissions. In this study, common-rail diesel engine was used with a cylinder volume of 1,500 cc. The engine was operated at 1,750 rpm and 40-100 Nm fixed engine loads. Besides, diesel fuel without any addition and L5Cu-added diesel fuel with four variables at 60 Nm of engine torque was examined by multivariateregression analysis, and pressure equations were produced. As a result, the engine at loads of 40 Nm and 60 Nm, which was also fed with L5Cu additive, was found to have reduced the specific fuel consumption and thermal efficiency by 1.9% and 0.73% respectively;however, at 80 Nm and 100 Nm loads, it was found to have increased by 0.3% and 0.7% respectively. It was concluded that the L5Cu additive at all engine loads caused a severe reduction in HC emission.
Oil production flow rate prediction is a critical aspect of oil and gas exploitation operations. Currently, flow rate forecasting is often estimated using theoretical or empirical models. Theoretical and empirical mod...
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Oil production flow rate prediction is a critical aspect of oil and gas exploitation operations. Currently, flow rate forecasting is often estimated using theoretical or empirical models. Theoretical and empirical models have limitations. This study applies an Artificial neural network (ANN) for prediction flow rate. The study considered 256 datasets collected from six wells in the HST Field, Cuu Long basin. The predicted results obtained from the ANN model with eight neurons and back-propagation algorithm achieved high predictability with a strong correlation coefficient of 0.964 and a low RMSE of 32.612 bbl/d. Therefore, the developed ANN models have been promised as an effective tool in production flow rate forecasting in oilfields.
The virtual propeller model can achieve the rapid numerical prediction of the ship self-propulsion performance through viscous flow, which used the improved body-force method. The two-dimensional lift coefficient CL a...
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ISBN:
(纸本)9780791858776
The virtual propeller model can achieve the rapid numerical prediction of the ship self-propulsion performance through viscous flow, which used the improved body-force method. The two-dimensional lift coefficient CL and the drag coefficient CD are very important parameters in this method, which are generally obtained by the potential flow methods and cannot incorporate viscous effects. This 'study will perform a fully nonlinear unsteady RANS (Reynolds Average Navier-Stokes) simulation to get the KP505 open -water characteristics and then divide its blade into several parts to get the lift coefficient CL and the drag coefficient CD on each one. Then fitting by multivariate regression method, the relationship between CL, CD and propeller parameters is obtained. The Unsteady Blade Element Theory (UBET) is coupled with RANS in house CFD code HUST-Ship (Hydrodynamic Unsteady Simulation Technology for Ship) to calculate the flow around the propeller. RANS equations are solved by the finite difference method and PISO arithmetic. have been made using structured grid with overset technology. The results show that comparing with the EFD data, the maximum differences ofthe result ofthe improved body-force method are 4.32% and 2.7% for the thrust coefficient and the torque coefficient respectively near the propeller operating point.
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