The mechanical behaviour of cylindrical GRP panels

Glass reinforced plastic (GRP) is now an established material for the fabrication of sonar windows. Its good mechanical strength, light weight, resistance to corrosion and acoustic transparency, are all properties which fit it for this application. This thesis describes a study, undertaken at the Royal Naval Engineering College, Plymouth, into the mechanical behaviour of a circular cylindrical sonar panel. This particular type of panel would be used to cover a flank array sonar in a ship or submarine. The case considered is that of a panel with all of its edges mechanically clamped and subject to pressure loading on its convex surface. A comprehensive program of testing, to determine the orthotropic elastic properties of the laminated composite panel material is described, together with a series of pressure tests on 1:5 scale sonar panels. These pressure tests were carried out in a purpose designed test rig, using air pressure to provide simulated hydrostatic and hydrodynamic loading. Details of all instrumentation used in the experimental work are given in the thesis. The experimental results from the panel testing are compared with predictions of panel behaviour obtained from both the Galerkin solution of Flugge's cylindrical shell equations (orthotropic case), and finite element modelling of the panels using PAFEC. A variety of appropriate panel boundary conditions are considered in each case. A parametric study, intended to be of use as a preliminary design tool, and based on the above Galerkin solution, is also presented. This parametric study considers cases of boundary conditions, material properties, and panel geometry, outside of those investigated in the experimental work Final conclusions are drawn and recommendations made regarding possible improvements to the procedures for design, manufacture and fixing of sonar panels in the Royal Navy.

Drying processess in the United Kingdom:assessment of industrial energy utilisation and efficiency of drying systems and the modelling of drying characters using neural networks (BL)

Drying is an important unit operation in process industry. Results have suggested that the energy used for drying has increased from 12% in 1978 to 18% of the total energy used in 1990. A literature survey of previous studies regarding overall drying energy consumption has demonstrated that there is little continuity of methods and energy trends could not be established. In the ceramics, timber and paper industrial sectors specific energy consumption and energy trends have been investigated by auditing drying equipment. Ceramic products examined have included tableware, tiles, sanitaryware, electrical ceramics, plasterboard, refractories, bricks and abrasives. Data from industry has shown that drying energy has not varied significantly in the ceramics sector over the last decade, representing about 31% of the total energy consumed. Information from the timber industry has established that radical changes have occurred over the last 20 years, both in terms of equipment and energy utilisation. The energy efficiency of hardwood drying has improved by 15% since the 1970s, although no significant savings have been realised for softwood. A survey estimating the energy efficiency and operating characteristics of 192 paper dryer sections has been conducted. Drying energy was found to increase to nearly 60% of the total energy used in the early 1980s, but has fallen over the last decade, representing 23% of the total in 1993. These results have demonstrated that effective energy saving measures, such as improved pressing and heat recovery, have been successfully implemented since the 1970s. Artificial neural networks have successfully been applied to model process characteristics of microwave and convective drying of paper coated gypsum cove. Parameters modelled have included product moisture loss, core gypsum temperature and quality factors relating to paper burning and bubbling defects. Evaluation of thermal and dielectric properties have highlighted gypsum's heat sensitive characteristics in convective and electromagnetic regimes. Modelling experimental data has shown that the networks were capable of simulating drying process characteristics to a high degree of accuracy. Product weight and temperature were predicted to within 0.5% and 5C of the target data respectively. Furthermore, it was demonstrated that the underlying properties of the data could be predicted through a high level of input noise.