Exact and simplified estimations for elastic buckling pressures of structural pipe-in-pipe cross sections under external hydrostatic pressure

Structural pipe-in-pipe cross sections have significant potential for application in offshore oil and gas production systems because they combine thermal insulation performance with structural strength and self weight in an integrated way. Such cross sections comprise inner and outer thin-walled pipes with the annulus between them fully filled by a selectable filler material to impart an appropriate combination of properties. Structural pipe-in-pipe cross sections can exhibit several different collapse mechanisms, and the basis of the preferential occurrence of one over the others is of interest. This article presents an exact analysis for predicting the elastic buckling behaviours of a structural pipe-in-pipe cross section when subjected to external hydrostatic pressure. Simplified approximations are also investigated for elastic buckling pressure and mode when the outer pipe and its contact with the filler material is considered as a pipe on an elastic foundation. Results are presented to show the variation of elastic buckling pressure with the relative elastic modulus of the filler and pipe materials, the filler thickness, and the thicknesses of the inner and outer pipes. Case studies based on realistic application scenarios are used to show that the simplified approximations are sufficiently accurate for practical structural design purposes.     

The relationship between transportation vulnerability,school attendance,and free transportation to an afterschool program for youth

Transportation - Transportation vulnerability, defined as lack of access to transportation resulting in financial, social, or health consequences, reduces quality of life. While research has...     

The influence of workplace environment on lung function of shopkeepers working near National Highway in Jalgaon: A note

The inferior ambient air quality was observed near highway passing through Jalgaon urban center. Among the pollutants critical level of particulates are observed at the roadside during May 2003 to April 2004. The shopkeepers working at the highway sides are at high risk of exposure to the air pollution caused by heavy highway traffic. The lung function test of the shopkeepers shows significant decrease in forced vital capacity, forced expiratory volume in one second and peak expiratory flow rate. The regular periodic health checkup and use of nose mask will protect the health of shopkeepers working near National Highway passing through Jalgaon urban center.     

NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

Summary Two NARX-type neural networks are developed for modelling nonlinear dynamic characteristics of passive twin-tube hydraulic dampers used in vehicle suspension systems. Quasi-isothermal and variable temperature NARX models are rigorously tested and compared with a state-of-the-art physical model proposed by Duym and Reybrouck (1998) and Duym (2000). Measured damper data, generated under isothermal and temperature varying conditions, is used for NARX training, physical model calibration, and predictive comparisons. Test kinematics include high amplitude sinusoidal displacements up to 14 Hz, and realistic random road profiles. The NARX models are trained via 'teacher forcing' and the feedforward backpropagation algorithm using both 'Early Stopping' and Bayesian Regularisation. Stable network design is also examined using the minimum posterior prediction error as the criterion for selecting a good network from a small number of tests. Calibration of the physical model proves highly complicated owing to considerable nonlinearity-in-the-parameters, requiring use of Sequential Quadratic Programming with an implicitly nonlinear constraint. The paper shows that NARX neural network modelling is vastly superior in terms of calibration efficiency, and prediction times, whilst offering roughly similar, if not better, model accuracy.