Evaluation of the ventilation system in an LNG cargo tank construction platform (CTCP) by the AHP-entropy weight method

Owing to the vulnerability of Invar to moisture in the membranes of LNG tanker cargo tank construction platforms(CTCPs),an energy-efficient ventilation system is needed to maintain a suitable thermal-moisture environment and a healthy workplace.However,the optimal distribution of supply and return devices that would guarantee worker satisfaction,air quality,and energy efficiency is unclear.Therefore,we conducted numerical simulations to determine the worker satisfaction indices(temperature,relative humidity,and carbon monoxide concentration satisfactions),air quality index(contaminant-removal efficiency),and energy efficiency index(heat-removal efficiency)with supply vane angles ranging from-75°to 0°and two return vent positions(the bottom return vent and the top return vent).The analytic hierarchy process(AHP)entropy weight method was employed to determine the optimal supply vane angle and return vent position for three design targets by considering these indices simultaneously.The results indicated that,with a supply angle of-45°and a bottom return vent,worker satisfaction and air quality were prioritized.Furthermore,a high energy performance of the ventilation system was achieved with a-15°supply angle and a bottom return vent.Moreover,a comprehensive graph of supply vane angles at both return heights,which could provide a reference for optimizing the ventilation system in LNG-CTCPs,is described.

Influence of Excitation Frequency on Slosh-Induced Impact Pressures of Liquefied Natural Gas Tanks

Liquid sloshing phenomena in No.2 tank of 140 km 3 liquefied natural gas (LNG) carriers have been studied numerically and experimentally.The scale of the model tank was selected as 1/55.9.Roll and pitch motions were tested.For measuring impact pressures,seventeen pressure sensors were installed on the tank model.A large number of excitation frequencies and filling heights were investigated.The experimental results showed that when the frequency of tank motion is close to the natural frequency of fluid inside the tank,large impact pressures may be caused.Resonance frequencies and maximum impact pressures of different filling height were presented.Among all the experimental situations,the maximum impact pressure always occurs at the place near 70% height of tank where should be especially concerned.A computational fluid dynamics (CFD) model was developed to simulate the sloshing in the tank.The model was based on the Reynolds-averaged Navier-Stokes (RANS) equations,with a standard κ-ε turbulence model.The volume of fluid (VOF) method was used to predict free surface elevations.Dynamic mesh technique was used to update the volume mesh.Computations for pressure time histories and peak pressures were compared to experimental results.Good agreement was observed.

Predicting saturated vapor pressure of LNG from density and temperature data with a view to improving tank pressure management

Determining the saturated vapor pressure(SVP)of LNG requires detailed thermodynamic calculations based on compositional data.Yet LNG compositions and SVPs evolve constantly for LNG stored in tanks.Moreover,the SVP of the LNG in a tank influences boil-off rates and tank pressure trends.In order to make improved tank pressure control decisions it would be beneficial for LNG tank operators to be made more constantly aware of the SVP of the LNG in a tank.Machine learning models that accurately estimate LNG SVP from density and temperature inputs offer the potential to provide such information.A dataset of five distinct,internationally traded LNG cargoes is compiled with 305 data records representing a range of temperature and density conditions.This can be used graphically to interpolate LNG SVP.However,two machine learning methods are applied to this dataset to automate the SVP predictions.A simple multi-layer perceptron artificial neural network(MLP-ANN)predicts SVP of the dataset with root mean square error(RMSE)=6.34 kPaA and R^(2)=0.975.The transparent open-box learning network(TOB),a regression-free optimized data matching algorithm predicts SVP of the dataset with RMSE=0.59 kPaA and R^(2)=0.999.When applied to infill unknown LNG compositions the superior TOB method achieves prediction accuracy of RMSE~3kPaA and R^(2)=0.996.Predicting LNG SVP to this level of accuracy is beneficial for tank-pressure management decision making.