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Modelling and control of a failing heart managed by a left ventricular assist device

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Son J. , Du D. , Du Y.

tom Vol. 40, no. 1

559--573

Left ventricular assist device (LVAD) recently has been used in advanced heart failure (HF), which supports a failing heart to meet blood circulation demand of the body. However, the pumping power of LVADs is typically set as a constant and cannot be freely adjusted to incorporate blood need from resting or mild exercise such as walking stairs. To promote the adoption of LVADs in clinical use as a long-term treatment option, a feedback controller is needed to regulate automatically the pumping power to support a time-varying blood demand, according to different physical activities. However, the tuning of pumping power induces suction, which will collapse the heart and cause sudden death. It is essential to consider suction when developing control strategy to adjust the pumping power. Further, hemodynamic of a failing heart exhibits variability, due to patient-to-patient heterogeneity and inherent stochastic nature of the heart. Such variability poses challenges for controller design. In this work, we develop a feedback controller to adjust the pumping power of an LVAD without inducing suction, while incorporating variability in hemodynamic. To efficiently quantify variability, the generalized polynomial chaos (gPC) theory is used to design a robust self-tuning controller. The efficiency of our control algorithm is illustrated with three case scenarios, each representing a specific change in physical activity of HF patients.

Probabilistic multiscale analysis of inelastic localized failure in solid mechanics

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Ibrahimbegovic A. , Matthies H. G.

tom Vol. 19, no. 3

277-304

In this work, we discuss the role of probability in providing the most appropriate multiscale based uncertainty quantification for the inelastic nonlinear response of heterogeneous materials undergoing localized failure. Two alternative approaches are discussed: i) the uncertainty quantification in terms of constructing the localized failure models with random field as parameters of failure criterion, ii) the uncertainty quantification in terms of the corresponding Bayesian updates of the corresponding evolution equation. The detailed developments are presented for the model problem of cement-based composites, with a two-phase meso-scale representation of material microstructure, where the uncertainty stems from the random geometric arrangement of each phase. Several main ingredients of the proposed approaches are discussed in detail, including microstructure approximation with a structured mesh, random field KLE representation, and Bayesian update construction. We show that the first approach is more suitable for the general case where the loading program is not known and the best one could do is to quantify the randomness of the general failure criteria, whereas the second approach is more suitable for the case where the loading program is prescribed and one can quantify the corresponding deviations. More importantly, we also show that models of this kind can provide a more realistic prediction of localized failure phenomena including the probability based interpretation of the size effect, with failure states placed anywhere in-between the two classical bounds defined by continuum damage mechanics and linear fracture mechanics.