arXiv subjects

Nausheen Basha

Publications and source records attributed to Nausheen Basha.

4 recordsLinked to original sources

arXiv2024

Machine Learning-Assisted Discovery of Flow Reactor Designs

Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensional with expensive optimisation limiting more complex solutions. To address this challenge, we establish a machine learning-assisted approach for the design of the next-generation of chemical reactors, combining the application of high-dimensional parameterisations, computational fluid dynamics, and multi-fidelity Bayesian optimisation. We associate the development of mixing-enhancing vortical flow structures in novel coiled reactors with performance, and use our approach to identify key characteristics of optimal designs. By appealing to the principles of flow dynamics, we rationalise the selection of novel design features that lead to experimental plug flow performance improvements of 60% over conventional designs. Our results demonstrate that coupling advanced manufacturing techniques with `augmented-intelligence' approaches can lead to superior design performance and, consequently, emissions-reduction and sustainability.

cs.CE↗

arXiv2023

Multi-Fidelity Data-Driven Design and Analysis of Reactor and Tube Simulations

The development of new manufacturing techniques such as 3D printing have enabled the creation of previously infeasible chemical reactor designs. Systematically optimizing the highly parameterized geometries involved in these new classes of reactor is vital to ensure enhanced mixing characteristics and feasible manufacturability. Here we present a framework to rapidly solve this nonlinear, computationally expensive, and derivative-free problem, enabling the fast prototype of novel reactor parameterizations. We take advantage of Gaussian processes to adaptively learn a multi-fidelity model of reactor simulations across a number of different continuous mesh fidelities. The search space of reactor geometries is explored through an amalgam of different, potentially lower, fidelity simulations which are chosen for evaluation based on weighted acquisition function, trading off information gain with cost of simulation. Within our framework we derive a novel criteria for monitoring the progress and dictating the termination of multi-fidelity Bayesian optimization, ensuring a high fidelity solution is returned before experimental budget is exhausted. The class of reactor we investigate are helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics, have the potential to be highly parameterized, and are easily manufactured using 3D printing. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming its mixing performance. In doing so we demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimization problems, supporting the design of the next generation of highly parameterized chemical reactors.

cs.CE↗

arXiv2023

Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian Optimisation-CFD approach

Processes involving the manufacture of fine/bulk chemicals, pharmaceuticals, biofuels, and waste treatment require plug flow characteristics to minimise their energy consumption and costs, and maximise product quality. One such versatile flow chemistry platform is the coiled tube reactor subjected to oscillatory motion, producing excellent plug flow qualities equivalent to well-mixed tanks-in-series 'N'. In this study, we discover the critical features of these flows that result in high plug flow performance using a data-driven approach. This is done by integrating Bayesian optimisation, a surrogate model approach, with Computational fluid dynamics that we treat as a black-box function to explore the parameter space of the operating conditions, oscillation amplitude and frequency, and net flow rate. Here, we correlate the flow characteristics as a function of the dimensionless Strouhal, oscillatory Dean, and Reynolds numbers to the reactor plug flow performance value 'N'. Under conditions of optimal performance (specific examples are provided herein), the oscillatory flow is just sufficient to limit axial dispersion through flow reversal and redirection, and to promote Dean vortices. This automated, open-source, integrated method can be easily adapted to identify the flow characteristics that produce an optimised performance for other chemical reactors and processes.

physics.flu-dyn↗

arXiv2022

Deep Gaussian Process-based Multi-fidelity Bayesian Optimization for Simulated Chemical Reactors

New manufacturing techniques such as 3D printing have recently enabled the creation of previously infeasible chemical reactor designs. Optimizing the geometry of the next generation of chemical reactors is important to understand the underlying physics and to ensure reactor feasibility in the real world. This optimization problem is computationally expensive, nonlinear, and derivative-free making it challenging to solve. In this work, we apply deep Gaussian processes (DGPs) to model multi-fidelity coiled-tube reactor simulations in a Bayesian optimization setting. By applying a multi-fidelity Bayesian optimization method, the search space of reactor geometries is explored through an amalgam of different fidelity simulations which are chosen based on prediction uncertainty and simulation cost, maximizing the use of computational budget. The use of DGPs provides an end-to-end model for five discrete mesh fidelities, enabling less computational effort to gain good solutions during optimization. The accuracy of simulations for these five fidelities is determined against experimental data obtained from a 3D printed reactor configuration, providing insights into appropriate hyper-parameters. We hope this work provides interesting insight into the practical use of DGP-based multi-fidelity Bayesian optimization for engineering discovery.

cs.CE↗