GPU-Based Fast Finite Element Solution for Nonlinear Anisotropic Material Behavior and Comparison of Integration Strategies

Štrbac, Vukašin; Pierce, David M.; Sloten, Jos Vander; Famaey, Nele

doi:10.1007/978-3-319-28329-6_9

Cited by 2 publications

(6 citation statements)

References 13 publications

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“…We note that researchers have been developing GPU‐based computational methods to accelerate FEA for biomedical applications . However, regardless using CPU or GPU for FEA, numerical stability and convergence issues still exist .…”

Section: Discussionmentioning

confidence: 99%

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Liang

Liu

Martin

et al. 2018

Numer Methods Biomed Eng

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Advances in structural finite element analysis (FEA) and medical imaging have made it possible to investigate the in vivo biomechanics of human organs such as blood vessels, for which organ geometries at the zero-pressure level need to be recovered. Although FEA-based inverse methods are available for zero-pressure geometry estimation, these methods typically require iterative computation, which are time-consuming and may be not suitable for time-sensitive clinical applications. In this study, by using machine learning (ML) techniques, we developed an ML model to estimate the zero-pressure geometry of human thoracic aorta given 2 pressurized geometries of the same patient at 2 different blood pressure levels. For the ML model development, a FEA-based method was used to generate a dataset of aorta geometries of 3125 virtual patients. The ML model, which was trained and tested on the dataset, is capable of recovering zero-pressure geometries consistent with those generated by the FEA-based method. Thus, this study demonstrates the feasibility and great potential of using ML techniques as a fast surrogate of FEA-based inverse methods to recover zero-pressure geometries of human organs.

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Section: Discussionmentioning

confidence: 99%

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Liang

Liu

Martin

et al. 2018

Numer Methods Biomed Eng

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“…Reports of large (up to two orders of magnitude) speed-ups can be found in the literature for the TLED method Taylor et al (2009), Strbac et al (2015), Strbac et al (2016), Bartezzaghi et al (2015). Such claims are often based on simplifications, some introduced in the following paragraphs.…”

Section: Gpu-accelerated Explicit Fe Analysismentioning

confidence: 99%

“…With the release of Compute Capability 1.3 devices in late 2008, fp64 computation is supported, but is still largely absent in literature. Bartezzaghi et al (2015) first addressed this issue, and with the exception of Strbac et al (2015) and Strbac et al (2016), the authors are unaware of reports using doubleprecision computation in our context. It is well-known that double-precision computation, particularly in atomic operations, is significantly slower but mandatory in accurate and general engineering application.…”

Section: Gpu-accelerated Explicit Fe Analysismentioning

confidence: 99%

“…Instead, for reasons of accuracy and reliability, the fully-integrated (FI, 2x2x2 integration points) quadrature scheme is advised, carrying a significant performance impact. With the exception of Strbac et al (2016), we are unaware of other reports that use full or selectively-reduced integration (SRI) in this context. In conclusion, for GPU-supported explicit FE, using double-precision, nearlyincompressible anisotropic materials, full quadrature, in the presence of degenerated hexahedral elements is unreported in literature (see Appendix A, and constitutes a complex and realistic scenario.…”

Section: Gpu-accelerated Explicit Fe Analysismentioning

confidence: 99%

“…While under-integration offers significant computational benefits generally (Strbac et al, 2016), it is presently inapplicable even with anti-hourglassing (Joldes et al, 2008;Schulz, 1985;Flanagan and Belytschko, 1981) due to strong anisotropy and collapsed elements.…”

Section: Potential For Further Speedupmentioning

confidence: 99%

See 2 more Smart Citations

Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

Štrbac

Pierce

Rodríguez-Vila

et al. 2017

Journal of Biomechanics

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Accurate estimation of peak wall stress (PWS) is the crux of biomechanically motivated rupture risk assessment for abdominal aortic aneurysms aimed to improve clinical outcomes. Such assessments often use the finite element (FE) method to obtain PWS, albeit at a high computational cost, motivating simplifications in material or element formulations. These simplifications, while useful, come at a cost of reliability and accuracy. We achieve research-standard accuracy and maintain clinically applicable speeds by using novel computational technologies. We present a solution using our custom finite element code based on graphics processing unit (GPU) technology that is able to account for added complexities involved with more physiologically relevant solutions, e.g. strong anisotropy and heterogeneity. We present solutions up to 17x faster relative to an established finite element code using state-of-the-art nonlinear, anisotropic and nearly-incompressible material descriptions. We show a realistic assessment of the explicit GPU FE approach by using complex problem geometry, biofidelic material law, doubleprecision floating point computation and full element integration. Due to the increased solution speed without loss of accuracy, shown on five clinical cases of abdominal aortic aneurysms, the method shows promise for clinical use in determining rupture risk of abdominal aortic aneurysms.

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GPU-Based Fast Finite Element Solution for Nonlinear Anisotropic Material Behavior and Comparison of Integration Strategies

Cited by 2 publications

References 13 publications

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta

Rupture risk in abdominal aortic aneurysms: A realistic assessment of the explicit GPU approach

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