Influence of random shrinkage porosity on equivalent elastic modulus of casting： A statistical and numerical approach

Shrinkage porosity is a type of random distribution defects and exists in most large castings. Different from the periodic symmetry defects or certain distribution defects, shrinkage porosity presents a random "cloud-like" configuration, which brings difficulties in quantifying the effective performance of defected casting. In this paper, the influences of random shrinkage porosity on the equivalent elastic modulus of QT400-18 casting were studied by a numerical statistics approach. An improved random algorithm was applied into the lattice model to simulate the "cloud-like" morphology of shrinkage porosity. Then, a large number of numerical samples containing random levels of shrinkage were generated by the proposed algorithm. The stress concentration factor and equivalent elastic modulus of these numerical samples were calculated. Based on a statistical approach, the effects of shrinkage porosity's distribution characteristics, such as area fraction, shape, and relative location on the casting's equivalent mechanical properties were discussed respectively. It is shown that the approach with randomly distributed defects has better predictive capabilities than traditional methods. The following conclusions can be drawn from the statistical simulations:(1) the effective modulus decreases remarkably if the shrinkage porosity percent is greater than 1.5%;(2) the average Stress Concentration Factor(SCF) produced by shrinkage porosity is about 2.0;(3) the defect's length across the loading direction plays a more important role in the effective modulus than the length along the loading direction;(4) the surface defect perpendicular to loading direction reduces the mean modulus about 1.5% more than a defect of other position.

A simple error estimation method for linear-regression-based thermal sharpening techniques with the consideration of scale difference

Thermal remote sensing imagery is helpful for land cover classification and related analysis.Unfortunately,the spatial resolution of thermal infrared(TIR)band is generally coarser than that of visual near-infrared band,which limits its more precise applications.Various thermal sharpening(TSP)techniques have been developed for improving the spatial resolution of the imagery of TIR band or land surface temperature(LST).However,there is no research on the theoretical estimation of TSP error till now,which implies that the error in sharpened LST imagery is unknown and the further analysis might be not reliable.In this paper,an error estimation method based on classical linear regression theory for the linear-regression-based TSP techniques was firstly introduced.However,the scale difference between the coarse resolution and fine resolution is not considered in this method.Therefore,we further developed an improved error estimation method with the consideration of the scale difference,which employs a novel term named equivalent random sample size to reflect the scale difference.A simulation study of modified TsHARP(a typical TSP technique)shows that the improved method estimated the TSP error more accurately than classical regression theory.Especially,the phenomena that TSP error increases with the increasing resolution gap between the initial and target resolutions can be successfully predicted by the proposed method.