Evaluation of the p-y method in the design of monopiles for offshore wind turbines

Monopile foundations, currently designed using the p-y method, are technically viable in supporting larger offshore wind turbines in waters to a depth of 30 m. The p-y method was developed to better understand the behavior of laterally loaded long slender piles required for the offshore oil and gas installations. The lateral load-deformation behavior of two monopiles, 5 and 7.5 m dia, installed in soft clays of varying undrained shear strength and stiffness, was studied. A combination of axial and lateral loads expected at an offshore wind farm location with a water depth of 30 m was used in the analysis. It was established that the Matlock (1970) p-y curves are too soft and under-estimate the ultimate soil reaction at all depths except at the monopile tip. At the pile tip, the base shear was not accounted for in the p-y curves, hence resulting in the over-estimation of the soil reaction. Consequently, the Matlock (1970) p-y formulation significantly underestimates the monopile ultimate lateral capacity. The use of the Matlock (1970) p-y method would result in over-conservative designs of monopiles for offshore wind turbines. This is an abstract of a paper presented at the Offshore Technology Conference (Houston, TX 5/6-9/2013).

Efficient Implementation of Spatially-Varying 3-D Ultrasound Deconvolution

Abstract—There are sometimes occasions when ultrasound beamforming is performed with only a subset of the total data that will eventually be available. The most obvious example is a mechanically-swept (wobbler) probe in which the three-dimensional data block is formed from a set of individual B-scans. In these circumstances, non-blind deconvolution can be used to improve the resolution of the data. Unfortunately, most of these situations involve large blocks of three-dimensional data. Furthermore, the ultrasound blur function varies spatially with distance from the transducer. These two facts make the deconvolution process time-consuming to implement. This paper is about ways to address this problem and produce spatially-varying deconvolution of large blocks of three-dimensional data in a matter of seconds. We present two approaches, one based on hardware and the other based on software. We compare the time they each take to achieve similar results and discuss the computational resources and form of blur model that each requires.