Search Results (2)

Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills. Full article

Maintaining offshore steel structures is challenging and not environmentally friendly due to the frequent visits for inspection and repairs. Some offshore lighthouses are equipped with carbon steel helidecks fixed onto their lantern galleries in the 1970s to provide easy and safe access to maintenance staff and inspectors. Even though the helidecks supporting structures have maintained their integrity and are still functional in the offshore harsh environmental conditions, their inspection and maintenance remains a challenge due to the need of frequent visits which requires flying to the location of the lighthouse to bring the maintenance staff and equipment. We have developed a multidisciplinary computational framework to design new generation of aluminium helidecks for offshore lighthouses. We calculated the wind speed at the location of the Bishop Rock lighthouse based on the meteorological data, and the load distribution on the helideck due to such a wind condition, using computational fluid dynamic analysis. Then, we used the calculated wind load with other mechanical loads in the events of normal and emergency landings of a helicopter on this structure to find the best design configuration for this helideck. We generated a design space for different configurations of a beam structure and carried out, static, transient and buckling analysis to assess each case using finite element method. The selection criterion was set to find the structure with the minimum volume fraction and compliance while keeping the stress below the allowable stress. We found the structure with eight vertical and circumferential sections featuring two rows of diagonal bracing with one at the base and the other one at the third section from the base of the helideck was the optimum design for the considered loading in this work. This framework can be adopted for the design and optimisation of other offshore structures by other researchers and designers. Full article