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Published in Phillip A. Laplante, Dictionary of Computer Science, Engineering, and Technology, 2017
intrinsic function a function that is recognized by the compiler. In some languages this means only that the function need not be explicitly declared, and in some languages or compilers it may also imply that the function may be compiled as an inline function.
Modelling the complexity of the foot and ankle during human locomotion: the development and validation of a multi-segment foot model using biplanar videoradiography
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2022
Jayishni N. Maharaj, Michael J. Rainbow, Andrew G. Cresswell, Sarah Kessler, Nicolai Konow, Dominic Gehring, Glen A. Lichtwark
Regardless of the model used, one difficulty in modelling motion of the foot is confirming the validity of the assumptions made in the model. The performance of some multi-segment foot models have been compared to bone pin data, and have demonstrated substantial mean differences in joint kinematics ranging from 3° to 8° (Nester et al. 2007). These differences are particularly alarming as some joints in the foot only have a total rotational range of 15°–20°. However, it is unclear whether the bone pin approach represents a true gold standard measure as it is invasive and soft-tissue movement can often cause pin bending, and thus errors in kinematics. Alternatively, biplanar videoradiography (BVR) is able to provide unique insight into the intrinsic function of the foot with high resolution and accuracy. Not bound to the limitations of bone pins and optical motion capture, BVR combines high-speed X-ray image sequences with 3D bone volumes obtained from computed tomography (CT) scans to track 3D bone movement of individual bones. With its excellent measurement accuracy, within 1 mm in translation and 1° in orientation in the foot (Wang et al. 2015) and high resolution, BVR has great potential to help develop and assess the accuracy of multi-segment foot models.
Modelling laminar diffusion flames using a fast convergence three-dimensional CVFEM code
Published in Combustion Theory and Modelling, 2021
Fábio Alfaia da Cunha, Carlos Alberto Gurgel Veras
Simulations were performed in a desktop computer with an Intel® CoreTM i7-6700k processor, Graphics card Geforce® GTX 1080Ti, 64GB DDR4 memory, 480GB SSD and Windows 10 Pro 64-bit OS. The tools for parallel computing were activated for faster calculations through multicore processing. The resulted discretisation equations were solved by a MATLAB intrinsic function employing the Generalised Minimum Residual method (GMRES) with double-precision floating-point. The code can be set to run by either, the computer’s CPU or the GPU, following recommendations in the MATLAB user guide [29]. Data transfer from the CPU to the GPU, and vice-versa, was set through the GPUArray and Gather commands in MATLAB, respectively. The code was also vectorised to take the most from the processing capacity of an off-the-shelf desktop computer. In parallel processing, care must be taken to avoid unnecessary data transfer between the CPU and the GPU devise memories as pointed by [30]. Therefore, the code was set to transfer data between the processing units exclusively at the start and the end of the computation. In that case, the number of nodes and variables for a given problem simulation should not exceed the GPU’s memory capacity.
Modelling rainfall-induced mudflows using FEMLIP and a unified hydro-elasto-plastic model with solid-fluid transition
Published in European Journal of Environmental and Civil Engineering, 2018
A simulation model that comprehensively describes the solid–fluid behaviours of geomaterials must include the following three capacities:Internal variables trackings. Landslides comprise geomaterials with an elasto-plastic behaviour in their solid-like phase. Internal variables associated with the material points (i.e. hardening parameters and plastic strains) must be tracked in time and space to calculate plasticity, which is an intrinsic function of the load history.Strict pursuit of the materials in space. Simulation involves different materials, their interactions, their spatial movements and the demand to strictly identify the materials positions over time.Large displacement solutions. Landslides, especially mudflows, require not only small transformations in a solid state, but also large displacements as the geomaterials spread over long distances in their fluid state.