| One of the significant scientific tasks of China’s Mars Exploration Project is the detection of water ice,which is of great importance in understanding the climate change of Mars and conducting in situ resource utilization.However,the current heat and mass transfer models for Martian soil and water ice do not take into account the seasonally varying atmospheric conditions,which makes it difficult to understand the states of seasonal subsurface water ice at low to midlatitudes and is unfavorable for the efficient mining of water ice based on the ice sublimation.The development of heat and mass transfer models for soil and water ice under Martian conditions and their application can provide a theoretical basis and technical support for the detection and in situ utilization of water ice on Mars.In this work,considering seasonally varying atmospheric conditions,we establish a coupled heat and mass transfer model for soil and water ice of Mars,and apply it to predict the states of seasonal subsurface water ice in the low to midlatitude regions and to conduct simulations of in situ thermal mining of water ice.First,to reduce the high time cost in the retrieval of thermal inertia,which is an important parameter influencing the stability of water ice,an intelligent model for predicting the Martian thermal inertia was established by using the machine learning.Second,for the problem of neglecting the varying radiation of the atmosphere in the ice model,a coupled heat and mass transfer model for soil and water ice of Mars was established by combining with seasonally varying solar and infrared radiations.Third,in order to investigate the states of seasonal subsurface ice,based on the coupled heat and mass transfer model,considering the varying atmosphere,the simulation of seasonal subsurface water ice at northern low to midlatitudes on Mars was performed.Finally,for the problem of low efficiency of in situ thermal mining of water ice,combining with the features of ice sublimation on Mars,a prediction model for the amount of produced water vapor during the thermal mining of water ice with conducting rods was developed,and the simulation of in situ thermal mining of water ice was performed.The optimization method for the mining scheme was established.It is found that the intelligent model for Martian thermal inertia could yield rapid and accurate predictions,where the prediction accuracy for different types of thermal inertia such as fine particles and sand is high up to 96%.The seasonal subsurface water ice is affected by the soil or atmospheric parameters such as thermal inertia and dust optical depth.Smaller thermal inertia,which is easier for the soil surface to cool down,is more conducive to the subsurface ice.Larger dust optical depth could be more unfavorable for the subsurface ice when the thermal effect of dust in the atmosphere dominates.With the variation in the solar longitude(Ls)which characterizes the Martian season,the seasonal subsurface water ice at northern low to midlatitudes mainly exists at Ls=220°-360°with the maximum amount at about 300°,and the variation in the amount of subsurface ice is closely related to the adsorption/desorption of water in the soil.For the efficient in situ thermal mining of water ice based on the ice sublimation,there is a balance between the water ice content in the ice layer and the separation distance of conducting rods in the multi-rods scheme.Higher water ice content of ice layer,which indicates larger thermal inertia,could be more unfavorable for the temperature rise,resulting in lower sublimation rate of water ice.And in this case,the separation distance of conducting rods could be reduced to keep a high sublimation rate. |
