Numerical Model to Predict Hemolysis and Transport in a Membrane-Based Microfluidic Device

Microfluidics has become an increasingly popular tool in the design and development of medical devices and artificial organs. Two promising applications of microfluidics are dialyzers and oxygenators. As a step toward portable dialysis treatment, continuous microfluidic dialysis may resolve many clinical issues with current dialysis treatments. Additionally, commercially available oxygenators exceed the blood volume of neonatal patients; low-volume microfluidic devices may safely deliver oxygen to these patients. Two critical parameters in the development of these devices is mechanical hemolysis and membrane diffusion, which are intricately connected to the geometry, flow rate, properties of the membrane, and each other. A computational model is developed to elucidate the connection between these phenomena to guide the design and optimization of these devices. In vitro experiments are conducted to validate the model. Importantly, a subset of hemolysis models agrees with experimental data, which is consistent with the literature. Additionally, the effect of microfluidic mixing elements that perturb flow near the membrane interface are studied in silico and in vitro. These data reveal that herringbone mixing elements increase hemolysis by 10% and flux across the membrane interface by 38% in silico and a statistically significant difference between smooth and herringbone devices is observed for a subset of devices tested. Furthermore, 10 of 18 computational models of hemolysis are shown to be statistically similar to experimental data. The agreement of these results suggest that finite element analysis may be able to quantitively model important factors in the design of microfluidic oxygenators and dialyzers

On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.Comment: 27 pages, 10 figure

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts