Photoelastic fringe patterns for stress analysis are investigated by use of hybrid technique and fringe phase shift method. The first one is a hybrid method which combines the conformal mapping technique and measured ...Photoelastic fringe patterns for stress analysis are investigated by use of hybrid technique and fringe phase shift method. The first one is a hybrid method which combines the conformal mapping technique and measured data away from the edge of a geometric discontinuity. Photoelastic data are hybridized with complex variable/mapping techniques to calculate photoelastic stress-field around a circular hole or an elliptical hole in plates under uniaxial tensile loading. This method determines full-field stresses in perforated finite tensile plates containing either a circular or an elliptical hole. The second one is a fringe phase shift method to separate isochromatics and isoclinics from photoelastic fringes of a circular disk under diametric compression by use of phase shift method. Digitally determined isochromatics and isoclinics are agreed well with those of manual measurements.展开更多
Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak t...Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.展开更多
文摘Photoelastic fringe patterns for stress analysis are investigated by use of hybrid technique and fringe phase shift method. The first one is a hybrid method which combines the conformal mapping technique and measured data away from the edge of a geometric discontinuity. Photoelastic data are hybridized with complex variable/mapping techniques to calculate photoelastic stress-field around a circular hole or an elliptical hole in plates under uniaxial tensile loading. This method determines full-field stresses in perforated finite tensile plates containing either a circular or an elliptical hole. The second one is a fringe phase shift method to separate isochromatics and isoclinics from photoelastic fringes of a circular disk under diametric compression by use of phase shift method. Digitally determined isochromatics and isoclinics are agreed well with those of manual measurements.
文摘Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.
