A two-grid iterative approach for real time haptics mediated interactive simulation of deformable objects

Author(s):  
Venkata S Arikatla ◽  
Suvranu De
2011 ◽  
Vol 20 (4) ◽  
pp. 289-308 ◽  
Author(s):  
Suvranu De ◽  
Dhannanjay Deo ◽  
Ganesh Sankaranarayanan ◽  
Venkata S. Arikatla

While an update rate of 30 Hz is considered adequate for real-time graphics, a much higher update rate of about 1 kHz is necessary for haptics. Physics-based modeling of deformable objects, especially when large nonlinear deformations and complex nonlinear material properties are involved, at these very high rates is one of the most challenging tasks in the development of real-time simulation systems. While some specialized solutions exist, there is no general solution for arbitrary nonlinearities. In this work we present PhyNNeSS—a Physics-driven Neural Networks-based Simulation System—to address this long-standing technical challenge. The first step is an offline precomputation step in which a database is generated by applying carefully prescribed displacements to each node of the finite element models of the deformable objects. In the next step, the data is condensed into a set of coefficients describing neurons of a Radial Basis Function Network (RBFN). During real-time computation, these neural networks are used to reconstruct the deformation fields as well as the interaction forces. We present realistic simulation examples from interactive surgical simulation with real-time force feedback. As an example, we have developed a deformable human stomach model and a Penrose drain model used in the Fundamentals of Laparoscopic Surgery (FLS) training tool box. A unique computational modeling system has been developed that is capable of simulating the response of nonlinear deformable objects in real time. The method distinguishes itself from previous efforts in that a systematic physics-based precomputational step allows training of neural networks which may be used in real-time simulations. We show, through careful error analysis, that the scheme is scalable, with the accuracy being controlled by the number of neurons used in the simulation. PhyNNeSS has been integrated into SoFMIS (Software Framework for Multimodal Interactive Simulation) for general use.


Author(s):  
Simone Ferrari ◽  
Simone Ambrogio ◽  
Andrew J Narracott ◽  
Adrian Walker ◽  
Paul D Morris ◽  
...  

Abstract Medical device design for personalised medicine requires sophisticated tools for optimisation of biomechanical and biofluidic devices. This paper investigates a new real-time tool for simulating structural and fluid scenarios - ANSYS Discovery Live - and we evaluate its capability in the fluid domain through benchmark flows that all involve steady state flow at the inlet and zero pressure at the outlet. Three scenarios are reported: i. Laminar flow in a straight pipe, ii. vortex shedding from the Karman Vortex, and iii. nozzle flows as characterised by an FDA benchmark geometry. The solver uses a Lattice Boltzmann method requiring a high performance GPU (nVidiaGTX1080, 8GB RAM). Results in each case were compared with the literature and demonstrated credible solutions, all delivered in near real-time: i. The straight pipe delivered parabolic flow after an appropriate entrance length (plug flow inlet conditions), ii. the Karman Vortex demonstrated appropriate vortex shedding as a function of Reynolds number, characterised by Strouhal number in both the free field and within a pipe, and ii the FDA benchmark geometry generated results consistent with the literature in terms of variation of velocity along the centreline and in the radial direction, although deviation from experimental validation was evident in the sudden expansion section of the geometry. This behaviour is similar to previous reported results from Navier-Stokes solvers. A cardiovascular stenosis example is also considered, to provide a more direct biomedical context. The current software framework imposes constraints on inlet/outlet boundary conditions, and only supports limited control of solver discretization without providing full field vector flow data outputs. Nonetheless, numerous benefits result from the interactive interface and almost-real-time solution, providing a tool that may help to accelerate the arrival of improved patient-specific medical devices.


2017 ◽  
Vol 37 (1) ◽  
pp. 56-68 ◽  
Author(s):  
Ibai Leizea ◽  
Ainitze Mendizabal ◽  
Hugo Alvarez ◽  
Iker Aguinaga ◽  
Diego Borro ◽  
...  

2002 ◽  
Vol 15 (4) ◽  
pp. 250-254
Author(s):  
Xi-mei XUE ◽  
Guang YANG ◽  
Tian-miao WANG

2017 ◽  
Vol 32 (6) ◽  
pp. 1198-1213 ◽  
Author(s):  
Shi-Yu Jia ◽  
Zhen-Kuan Pan ◽  
Guo-Dong Wang ◽  
Wei-Zhong Zhang ◽  
Xiao-Kang Yu

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