Computational fluid dynamics (CFD) provides a flexible tool for investigation of separation processes within membrane hollow fiber modules. By enabling a three-dimensional and time dependent description of the corresponding transport phenomena, very detailed information about mass transfer or geometrical influences can be provided. The high level of detail comes with high computational costs, especially since species transport simulations must discretize and resolve steep gradients in the concentration polarization layer at the membrane. In contrast, flow simulations are not required to resolve these gradients. Hence, there is a large gap in the scale and complexity of computationally feasible geometries when comparing flow and species transport simulations. A method, which tries to cover the mentioned gap, is presented in the present article. It allows upscaling of the findings of species transport simulations, conducted for reduced geometries, on the geometrical scales of flow simulations. Consequently, total transmembrane transport of complete modules can be numerically predicted. The upscaling method does not require any empirical correlation to incorporate geometrical characteristics but solely depends on results acquired by CFD flow simulations. In the scope of this research, the proposed method is explained, conducted, and validated. This is done by the example of CO2 removal in a prototype hollow fiber membrane oxygenator.

CO2 removal via membrane oxygenators during lung protective ventilation has become a reliable clinical technique. For further optimization of oxygenators, accurate prediction of the CO2 removal rate is necessary. It can either be determined by measuring the CO2 content in the exhaust gas of the oxygenator (sweep flow-based) or using blood gas analyzer data and a CO2 solubility model (blood-based). In this study, we determined the CO2 removal rate of a prototype oxygenator utilizing both methods in in vitro trials with bovine and in vivo trials with porcine blood. While the sweep flow-based method is reliably accurate, the blood-based method depends on the accuracy of the solubility model. In this work, we quantified performances of four different solubility models by calculating the deviation of the CO2 removal rates determined by both methods. Obtained data suggest that the simplest model (Loeppky) performs better than the more complex ones (May, Siggaard-Anderson, and Zierenberg). The models of May, Siggaard-Anderson, and Zierenberg show a significantly better performance for in vitro bovine blood data than for in vivo porcine blood data. Furthermore, the suitability of the Loeppky model parameters for bovine blood (in vitro) and porcine blood (in vivo) is evaluated.

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Blood pumps have found applications in heart support devices, oxygenators, and dialysis systems, among others. Often, there is no room for sensors, or the sensors are simply unreliable when long-term operation is required. However, control systems rely on those hard-to-measure parameters, such as blood flow rate and pressure difference, thus their estimation takes a central role in the development process of such medical devices. The viscosity of the blood not only influences the estimation of those parameters but is often a parameter that is of great interest to both doctors and engineers. In this work, estimation methods for blood flow rate, pressure difference, and viscosity are presented using Gaussian process regression models. Different water–glycerol mixtures were used to model blood. Data was collected from a custom-built blood pump, designed for intracorporeal oxygenators in an in vitro test circuit. The estimation was performed from motor current and motor speed measurements and its accuracy was measured for: blood flow rate r2 = 0.98, root mean squared error (RMSE) = 46 mL.min−1; pressure difference r2 = 0.98, RMSE = 8.7 mmHg; and viscosity r2 = 0.98, RMSE = 0.049 mPa.s. The results suggest that the presented methods can be used to accurately predict blood flow rate, pressure, and viscosity online.

Objective: Electrical impedance tomography (EIT) is a non-invasive and relatively cheap imaging technique allowing continuous monitoring of lung function at the bedside. However, image reconstruction and processing are not yet standardized for clinical use, limiting comparability and reproducibility between studies. In addition, optimal reconstruction settings still have to be identified for different clinical applications. In this work (i) a systematic way to select ‘good’ EIT algorithm parameters is developed and (ii) an evaluation of these parameters in terms of correct functional imaging and consistency is performed. Approach: First, 19 200 reconstruction models are generated by full factorial design of experiment in 5D space. Then, in order to quantify the quality of reconstruction, known conductivity changes are introduced and figures of merit (FoM) are calculated from the response image. These measures are further used to select a subset of reconstruction models, matching certain FoM thresholds, and are then used for in vivo evaluation. For this purpose, EIT images of one piglet are reconstructed to assess changes in tidal impedance and end-expiratory lung impedance, at positive end expiratory pressure of 0 and 15 cmH2O. From ground truth spirometry measurements, physiological criteria are formulated and the subset of models is further reduced. Finally, the remaining reconstruction models are evaluated on physiological data gathered from published data in the literature to assess the generalization possibilities. Main results: Parametrization of EIT image reconstruction has a strong influence on the resulting FoM and the derived physiological parameter. While numerous reconstruction models showed reasonable values for a single parameter, in total only 12 matched all simulation and physiological criteria. After validation on further physiological data, only a single reconstruction model remained with a noise figure of 0.3, target size of 0.08, weight radius of 0.3, normalized voltage and strong weighting of lung and heart regions. Furthermore, the relationship between the reconstruction settings and some FoM could be partly explained by using a linear statistical model. Significance: The quest for standard reconstruction settings is highly relevant for future clinical applications. Simulation measures might help to assess the quality of the reconstruction models, but further evaluation of more data and different experimental settings is required.

Extracorporeal membrane oxygenators are essential medical devices for the treatment of patients with respiratory failure. A promising approach to improve oxygenator performance is the use of microstructured hollow fiber membranes that increase the available gas exchange surface area. However, by altering the traditional circular fiber shape, the risk of low flow, stagnating zones that obstruct mass transfer and encourage thrombus formation, may increase. Finding an optimal fiber shape is therefore a significant task. In this study, experimentally validated computational fluid dynamics simulations were used to investigate transverse flow within fiber packings of circular and microstructured fiber geometries. A numerical model was applied to calculate the local Sherwood number on the membrane surface, allowing for qualitative comparison of gas exchange capacities in low-velocity areas caused by the microstructured geometries. These adverse flow structures lead to a tradeoff between increased surface area and mass transfer. Based on our simulations, we suggest an optimal fiber shape for further investigations that increases potential mass transfer by up to 48% in comparison to the traditional, circular hollow fiber shape.

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