How to implement user-defined fiber-reinforced hyperelastic materials in finite element software

Fehervary, Heleen; Maes, Lauranne; Vastmans, Julie; Kloosterman, Gertjan; Famaey, Nele

doi:10.1016/j.jmbbm.2020.103737

Cited by 20 publications

(4 citation statements)

References 17 publications

(24 reference statements)

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“…The constitutive behavior was defined by employing the UANISOHYPER_INV user subroutine. For more information about the UANISOHYPER_INV, the reader is referred to the Abaqus User Subroutines Reference Manual (Abaqus 2021) and (Fehervary et al 2020).…”

Section: Unloaded Shape Estimationmentioning

confidence: 99%

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Torbati,

Daneshmehr,

Pouraliakbar

et al. 2024

Biomech Model Mechanobiol

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Biomechanics-based patient-specific modeling is a promising approach that has proved invaluable for its clinical potential to assess the adversities caused by ischemic heart disease (IHD). In the present study, we propose a framework to find the passive material properties of the myocardium and the unloaded shape of cardiac ventricles simultaneously in patients diagnosed with ischemic cardiomyopathy (ICM). This was achieved by minimizing the difference between the simulated and target end-diastolic pressure-volume relationships (EDPVRs) using black-box Bayesian optimization, based on the finite element analysis (FEA).End-diastolic (ED) biventricular geometry and the location of the ischemia were determined from cardiac magnetic resonance (CMR) imaging. We employed our pipeline to model the cardiac ventricles of three patients aged between 57 and 66 years, with and without the inclusion of valves. An excellent agreement between the simulated and target EDPVRs has been reached. Our results revealed that the incorporation of valvular springs typically leads to lower hyperelastic parameters for both healthy and ischemic myocardium, as well as a higher fiber Green strain in the viable regions compared to models without valvular stiffness. Furthermore, the addition of valve-related effects did not result in significant changes in myofiber stress after optimization. We concluded that more accurate results could be obtained when cardiac valves were considered in modeling ventricles. The present novel and practical methodology paves the way for developing digital twins of ischemic cardiac ventricles, providing a non-invasive assessment for designing optimal personalized therapies in precision medicine.

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Section: Unloaded Shape Estimationmentioning

confidence: 99%

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Torbati,

Daneshmehr,

Pouraliakbar

et al. 2024

Biomech Model Mechanobiol

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“…The accurate simulation of cardiovascular disease is a complex challenge that requires collective efforts across a multitude of disciplines including cardiovascular medicine, applied mathematics, biomechanics, and computer science [44]. Clearly, it is impossible that everyone has a specialized training in material modeling and an in-depth knowledge in finite element simulation [10]. However, selecting a poor material model does not only jeopardize the success of the entire simulation, but can have life-threatening consequences for the patient.…”

Section: Motivationmentioning

confidence: 99%

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Peirlinck,

Linka,

Hurtado

et al. 2023

Preprint

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Personalized computational simulations have emerged as a vital tool to understand the biomechanical factors of a disease, predict disease progression, and design personalized intervention. Material modeling is critical for realistic biomedical simulations, and poor model selection can have life-threatening consequences for the patient. However, selecting the best model requires a profound domain knowledge and is limited to a few highly specialized experts in the field. Here we explore the feasibility of eliminating user involvement and automate the process of material modeling in finite element analyses. We leverage recent developments in constitutive neural networks, machine learning, and artificial intelligence to discover the best constitutive model from thousands of possible combinations of a few functional building blocks. We integrate all discoverable models into the finite element workflow by creating a universal material subroutine that contains more than 60,000 models, made up of 16 individual terms. We prototype this workflow using biaxial extension tests from healthy human arteries as input and stress and stretch profiles across the human aortic arch as output. Our results suggest that constitutive neural networks can robustly discover various flavors of arterial models from data, feed these models directly into a finite element simulation, and predict stress and strain profiles that compare favorably to the classical Holzapfel model. Replacing dozens of individual material subroutines by a single universal material subroutine–populated directly via automated model discovery–will make finite element simulations more user-friendly, more robust, and less vulnerable to human error. Democratizing finite element simulation by automating model selection could induce a paradigm shift in physics-based modeling, broaden access to simulation technologies, and empower individuals with varying levels of expertise and diverse backgrounds to actively participate in scientific discovery and push the boundaries of biomedical simulation.

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“…To account for the FRE + , the HGO material law is modified, namely, the Macaulay bracket is omitted. This modification is possible using the user subroutine provided in [4]. Therein, the original HGO material law has been reimplemented and the source code only needs to be changed accordingly.…”

Section: Materials Modelsmentioning

confidence: 99%

Simulations of the snap‐through behavior of a fiber reinforced elastomer structure for the design of a simple clamping mechanism

Schasching

Duy

Todt

et al. 2023

Proc Appl Math and Mech

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A glass fiber reinforced elastomer (FRE) sheet is used to design a simple clamping mechanism for prospective engineering applications. The mechanism is based on the phenomena of snap-through buckling of a segment of a shallow cylindrical shell. It is simply supported at the straight edges and actuated by edge moments. The main objective of such a mechanism is the resulting clamping force, being related to the response of the material under bending. Bending includes compressive stresses, and since the fibers are embedded in a very soft matrix, it is important to understand their contribution to the compressive stiffness of the FRE sheet. For this purpose, numerical simulations by means of the Finite Element Method are performed and a simulation strategy for predicting the clamping force of the mechanism is presented. The clamping forces predicted for FRE composites with and without the contribution of fibers to the compressive stiffness are compared to that of the pure elastomer. The results illustrate the potential of FRE based structures in mechanism-like applications. If an adequate clamping force is desired, the pure elastomer is not suitable for being used in this kind of clamping mechanism and the fiber reinforcement is necessary. If the fibers contribute to the compressive stiffness, a significantly higher clamping force is predicted. Furthermore, the FRE based structure shows a complex snap-through deformation pattern, which has to be taken into account in the design of the mechanism and which requires non-trivial simulation strategies.

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How to implement user-defined fiber-reinforced hyperelastic materials in finite element software

Cited by 20 publications

References 17 publications

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Personalized evaluation of the passive myocardium in ischemic cardiomyopathy via computational modeling using Bayesian optimization

Democratizing biomedical simulation through automated model discovery and a universal material subroutine

Simulations of the snap‐through behavior of a fiber reinforced elastomer structure for the design of a simple clamping mechanism

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