In the present study, a probabilistic finite element tool was assessed using an uncemented total hip replacement model. Fully bonded and frictional interfaces were investigated for combinations of three proximal femurs and two implant designs, the Proxima short stem and the IPS hip stem prostheses. The Monte Carlo method was used with two performance indicators: the percentage of bone volume that exceeded specified strain limits and the maximum nodal micromotion. The six degrees of freedom of bone-implant relative position, magnitude of the hip contact force (L), and spatial direction of L were the random variables. The distal portion of the proximal femurs was completely constrained and some of the main muscle forces acting in the hip were applied. The coefficients of the linear approximation between the random variables and the output were used as the sensitivity values. In all cases, bone-implant position related parameters were the most sensitive parameters. The results varied depending on the femur, the implant design and the interface conditions. Values of maximum nodal micromotion agreed with results from previous studies, confirming the robustness of the implemented computational tool. It was demonstrated that results from a single model study should not be generalised to the entire population of femurs and that bone variability is an important factor that should be investigated in such analyses.

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In the present study, a probabilistic finite element tool was implemented to assess an uncemented total hip replacement including variability in bone-implant version angle. The Monte Carlo method was used with two different performance indicators: the bone maximum nodal von-Mises elastic strain and the bone volume (BV) percentage exceeding specified strain limits. Implant version, bone stiffness and load magnitude were the most sensitive parameters. The results were more consistent using percentage BV under specified limit strains as the performance indicator, even for a low number of simulations. The reliability of the computational tool was demonstrated through a comparison with previous studies, and the consistency of the results for all strain limits investigated.

Abstract-It is well-known that autonomous underwater vehicle (AUV) missions are a challenging, high-risk robotics application. With many parallels to Mars rovers, AUV missions involve operating a vehicle in an inherently uncertain environment of which our prior knowledge is often sparse or low-resolution. The lack of an accurate prior, coupled with poor situational awareness and potentially significant sensor noise, presents substantial engineering challenges in navigation, localisation, state estimation and control. When constructing missions and operating AUVs, it is important to consider the risks involved. Stakeholders need to be reassured that risks of vehicle loss or damage have been minimised where possible, and scientists need to be confident that the mission is likely to produce sufficient high-quality data to meet the aims of the deployment. In this paper, we consider the challenges associated with risk analysis methods and representations for multi-vehicle missions, reviewing the relevant literature and proposing a methodology.

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