This study focusses on the fluid mechanic analysis and performance assessment of a one-phase swirling flow multi-nozzle annular jet pump using Reynoldsaveraged Navier-Stokes simulations and experimental measurements carried out with a bespoke test rig. The numerical investigation of the flow physics of the device, key to understanding its fluid dynamics and optimising its performance, is made particularly challenging by the existence of flow swirl. Thus, the predictive capabilities of two alternative approaches for the turbulence closure of the Reynolds-averaged Navier-Stokes equations, namely the k − ω shear stress transport and the Reynolds stress models, are assessed against measured static pressure fields for three regimes characterised by different swirl strength, and a thorough cross-comparative analysis of the flow physics using the two closures is performed to complement the information provided by the experimental measurements. At the lowest swirl level, the two simulation types are in very good agreement, and they both agree very well with the measured static pressure fields. As the flow swirl increases, the two numerical results differ more and the Reynolds stress model is in better agreement with the measured static pressure.At the highest swirl level the shear stress transport analysis predicts weaker dis-Fully documented templates are available in the elsarticle package on CTAN.

Additive manufacturing (AM) is the name given to a family of manufacturing processes where materials are joined to make parts from 3D modelling data, generally in a layer-upon-layer manner. AM is rapidly increasing in industrial adoption for the manufacture of end-use parts, which is therefore pushing for the maturation of design, process, and production techniques. Machine learning (ML) is a branch of artificial intelligence concerned with training programs to self-improve and has applications in a wide range of areas, such as computer vision, prediction, and information retrieval. Many of the problems facing AM can be categorised into one or more of these application areas. Studies have shown ML techniques to be effective in improving AM design, process, and production but there are limited industrial case studies to support further development of these techniques.

A study into reinforcing the hull of the recently developed paired column semisubmersible platform has been carried out by understanding the stress profile around its columns from hydrodynamic interaction during survival and extreme weather conditions in the Gulf of Mexico. The conceptualization of this hull system is to enable dry-tree technology on semisubmersibles for deep-sea exploration. Its hydrodynamic response behaviour has been confirmed to be compatible with this technology, although its size and high steel requirement are of major disadvantage. Preliminary CFD study has showed an unusual flow behaviour within and around the hull due to its unique column arrangement. This behaviour creates an unusual hydrodynamic pressure profile on the hull, dominated by the wave parameters. Numerical models were developed using ANSYS and AQWA to compute the stress distribution on the columns from this unique uneven hydrodynamic pressure. The boundary conditions for the FE-model were formulated using hydrostatic stiffness theories and hydrodynamic response plots developed in Orcaflex. The results have showed high stress concentration on the inner columns. For operating conditions (low wave amplitude), the wave propagating direction was observed to have little or no effect on the column stress distribution. Significant effect of the wave propagating angle was observed as its amplitude gradually increases. Results for topside and deck mass effect on the stress distribution on the columns also suggested high stress distribution around the joint area of the inner columns for extreme and survival weather conditions, irrespective of the flow orientation.

This paper describes the most recent work on the development of the autonomous robot excavator -LUCIE 2 at Lancaster. Although the LUCIE project (Lancaster University Computerised Intelligent Excavator) has been running for about five years , in recent months it has undergone a radical change in both system hardware and software . This has come about as a result of research sponsored by the UK Safety Critical Systems Programme , which has highlighted the problems of producing a well argued safety case for intelligent robots in unstructured environments . Other changes have resulted from the automation of the vehicle tracks which means that LUCIE 2 is fully mobile for the first time. Work is also progressing on the problem of linking the robot excavator to project CAD drawings , and this is briefly discussed in the paper. The new developments discussed in the paper include : the use of three individual ultra-compact PC 104 computer systems, communication between computers using CAN bus , the use of a new scanning laser sensor to detect possible collisions , and the use of a satellite global positioning system for excavator positioning and navigation. Recent developments in GPS technology now mean that the vehicle can be reliably positioned to an accuracy of 25 mm.

ABSTRACT:Two applications for the use of a laser-scanning device are currently under investigation at Lancaster University. Lancaster University Computerised Intelligent Excavator (LUCIE) is an autonomous excavator which navigates using GPS and compass readings. Work is currently concentrating on navigational safety, for which the rotoscan sensor is employed for obstacle detection, and for possible self-localisation and environment comprehension in ambiguous operational states. Starlifter is a robotic arm built by Construction Robotics Ltd. The rotoscan sensor in this instance is to be mounted on the tool head and used as a final positioning navigation tool. Both these applications rely heavily on the interpretation of the received data, and the ability to filter out any interference. This paper initially outlines the mode of utilisation of the laser range finder within such applications and then proceeds to investigate the implications and potential limitations of such a sensor following the analysis of the sensory data from external field trials.