Solution electrospinning of particle-polymer composite fibres

Electrospinning is a fast, simple way to produce nano/microfibers, resulting in porous mats with a high surface to volume ratio. Another material with high surface to volume ratio is aerogel. A drawback of aerogels is its inherent mechanical weakness. To counteract this, aerogels can be embedded into scaffolds. The formation of a particle/polymer composite results in improved mechanical stability, without compromising the porosity. In the presented study, aerogel and poly(ethylene oxide) are mixed into a solution, and spun to thin fibres. Thereby a porous membrane, on the micro- and nano-scale, is produced. The maximum polymer-silica weight-ratio yielding stable fibres has also been determined. The morphology of the fibres at different weight ratios has been investigated by optical microscopy and scanning electron microscope (SEM). Low aerogel concentrations yield few particles located in polymer fibres, whereas higher amounts resulted in fibres dominated by the aerogel particle diameters. The diameters of these fibres were in the range between 13 um to 41 um. The flowrate dependence of the fibre diameter was evaluated for polymer solutions with high particle contents. The self-supporting abilities of these fibres are discussed. It is concluded that selfsupporting polymer/aerogel composites can be made by electrospinning

Reduced Order Modeling for Nonlinear PDE-constrained Optimization using Neural Networks

Nonlinear model predictive control (NMPC) often requires real-time solution to optimization problems. However, in cases where the mathematical model is of high dimension in the solution space, e.g. for solution of partial differential equations (PDEs), black-box optimizers are rarely sufficient to get the required online computational speed. In such cases one must resort to customized solvers. This paper present a new solver for nonlinear time-dependent PDE-constrained optimization problems. It is composed of a sequential quadratic programming (SQP) scheme to solve the PDE-constrained problem in an offline phase, a proper orthogonal decomposition (POD) approach to identify a lower dimensional solution space, and a neural network (NN) for fast online evaluations. The proposed method is showcased on a regularized least-square optimal control problem for the viscous Burgers' equation. It is concluded that significant online speed-up is achieved, compared to conventional methods using SQP and finite elements, at a cost of a prolonged offline phase and reduced accuracy.Comment: Accepted for publishing at the 58th IEEE Conference on Decision and Control, Nice, France, 11-13 December, https://cdc2019.ieeecss.org