Publications
18 results found
Botsas T, Mason LR, Pan I, 2022, Rule-based Bayesian regression, STATISTICS AND COMPUTING, Vol: 32, ISSN: 0960-3174
Botsas T, Pan I, Mason LR, et al., 2022, Multiphase flow applications of nonintrusive reduced-order models with Gaussian process emulation, Data-Centric Engineering, Vol: 3, ISSN: 2632-6736
Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can be used to predict the characteristics of multiphase flows. In previous work, we presented a ROM analysis framework that coupled compression techniques, such as autoencoders, with Gaussian process regression in the latent space. This pairing has significant advantages over the standard encoding–decoding routine, such as the ability to interpolate or extrapolate in the initial conditions’ space, which can provide predictions even when simulation data are not available. In this work, we focus on this major advantage and show its effectiveness by performing the pipeline on three multiphase flow applications. We also extend the methodology by using deep Gaussian processes as the interpolation algorithm and compare the performance of our two variations, as well as another variation from the literature that uses long short-term memory networks, for the interpolation.
Pan I, Mason LR, Matar OK, 2022, Data-centric engineering: integrating simulation, machine learning and statistics. challenges and opportunities, Chemical Engineering Science, Vol: 249, ISSN: 0009-2509
Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-disciplinary research activity with significant interest and investment from commercial stakeholders. Mechanistic models, based on physical equations, and purely data-driven statistical approaches represent two ends of the modelling spectrum. New hybrid, data-centric engineering approaches, leveraging the best of both worlds and integrating both simulations and data, are emerging as a powerful tool with a transformative impact on the physical disciplines. We review the key research trends and application scenarios in the emerging field of integrating simulations, machine learning, and statistics. We highlight the opportunities that such an integrated vision can unlock and outline the key challenges holding back its realisation. We also discuss the bottlenecks in the translational aspects of the field and the long-term upskilling requirements for the existing workforce and future university graduates.
Arvizu JM, Mason LR, Moriarty J, 2021, Reinforcing the role of competition platforms, PATTERNS, Vol: 2, ISSN: 2666-3899
Maulik R, Botsas T, Ramachandra N, et al., 2021, Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation, PHYSICA D-NONLINEAR PHENOMENA, Vol: 416, ISSN: 0167-2789
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- Citations: 15
PAUL S, HSU W-L, MAGNINI M, et al., 2021, Analysis and control of vapor bubble growth inside solid-state nanopores, Journal of Thermal Science and Technology, Vol: 16, Pages: 1-20, ISSN: 1880-5566
The increasing demands of computational power have accelerated the development of 3D circuits in the semiconductor industry. To resolve the accompanying thermal issues, two-phase microchannel heat exchangers using have emerged as one of the promising solutions for cooling purposes. However, the direct boiling in microchannels and rapid bubble growth give rise to highly unstable heat flux on the channel walls. In this regard, it is hence desired to control the supply of vapor bubbles for the elimination of the instability. In this research, we investigate a controllable bubble generation technique, which is capable of periodically producing bubble seeds at the sub-micron scale. These nanobubbles were generated in a solid-state nanopore filled with a highly concentrated electrolyte solution. As an external electric field was applied, the localized Joule heating inside the nanopore initiated the homogeneous bubble nucleation. The bubble dynamics was analyzed by measuring the ionic current variation through the nanopore during the bubble nucleation and growth. Meanwhile, we theoretically examined the bubble growth and collapse inside the nanopore by a moving boundary model. In both approaches, we demonstrated that by altering the pore size, the available sensible heat for the bubble growth can be manipulated, thereby offering the controllability of the bubble size. This unique characteristic renders nanopores suitable as a nanobubble emitter for microchannel heat exchangers, paving the way for the next generation microelectronic cooling applications.
Paul S, Hsu W-L, Magnini M, et al., 2020, Single-bubble dynamics in nanopores: Transition between homogeneous and heterogeneous nucleation, Physical Review Research, Vol: 2, Pages: 1-14, ISSN: 2643-1564
When applying a voltage bias across a thin nanopore, localized Joule heating can lead to single-bubble nucleation, offering a unique platform for studying nanoscale bubble behavior, which is still poorly understood. Accordingly, we investigate bubble nucleation and collapse inside solid-state nanopores filled with electrolyte solutions and find that there exists a clear correlation between homo/heterogeneous bubble nucleation and the pore diameter. As the pore diameter is increased from 280 to 525 nm, the nucleation regime transitions from predominantly periodic homogeneous nucleation to a nonperiodic mixture of homogeneous and heterogeneous nucleation. A transition barrier between the homogeneous and heterogeneous nucleation regimes is defined by considering the relative free-energy costs of cluster formation. A thermodynamic model considering the transition barrier and contact-line pinning on curved surfaces is constructed, which determines the possibility of heterogeneous nucleation. It is shown that the experimental bubble generation behavior is closely captured by our thermodynamic analysis, providing important information for controlling the periodic homogeneous nucleation of bubbles in nanopores.
Inguva P, Mason LR, Pan I, et al., 2020, Numerical simulation, clustering, and prediction of multicomponent polymer precipitation, Data-Centric Engineering, Vol: 1
<jats:title>Abstract</jats:title> <jats:p>Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.</jats:p>
Gonçalves GFN, Batchvarov A, Liu Y, et al., 2020, Data-driven surrogate modeling and benchmarking for process equipment, Data-Centric Engineering, Vol: 1, ISSN: 2632-6736
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimization. A suite of computational fluid dynamics (CFD) simulations geared toward chemical process equipment modeling has been developed and validated with experimental results from the literature. Various regression-based active learning strategies are explored with these CFD simulators in-the-loop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies and five regression techniques are compared, considering a set of four test cases of industrial significance and varying complexity. Gaussian process regression was observed to have a consistently good performance for these applications. The present quantitative study outlines the pros and cons of the different available techniques and highlights the best practices for their adoption. The test cases and tools are available with an open-source license to ensure reproducibility and engage the wider research community in contributing to both the CFD models and developing and benchmarking new improved algorithms tailored to this field.
Statham TM, Mason LR, Mumford KA, et al., 2015, The specific reactive surface area of granular zero-valent iron in metal contaminant removal: Column experiments and modelling, WATER RESEARCH, Vol: 77, Pages: 24-34, ISSN: 0043-1354
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- Citations: 17
Ciceri D, Mason LR, Harvie DJE, et al., 2014, Extraction kinetics of Fe(III) by di-(2-ethylhexyl) phosphoric acid using a Y-Y shaped microfluidic device, CHEMICAL ENGINEERING RESEARCH & DESIGN, Vol: 92, Pages: 571-580, ISSN: 0263-8762
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- Citations: 38
Mason LR, Stevens GW, Harvie DJE, 2014, Subgrid CFD film-drainage modelling: Application to buoyancy-driven droplet-wall collisions in emulsions
The buoyancy-driven interaction between a free droplet and solid wall is simulated. The presented model makes use of the continuum-surface-force (CSF) framework to couple an interface-capturing technique to a lower-dimensional lubrication reduction of the thin-film momentum equation. Droplet-scale fluid transport was modelled using an axisymmetric level-set method, including a reinitialisation procedure. Through comparison of model results with experimental data from the literature, we demonstrate the effectiveness of this approach in simulating the challenging problem of small-length-scale drainage in computational fluid dynamics (CFD) modelling. Film-thickness profiles were modelled up until the onset of asymmetric film-drainage. Results presented will aid in the future improvement of simulation techniques for droplet interactions in liquid-liquid systems.
Ciceri D, Mason LR, Harvie DJE, et al., 2013, Modelling of interfacial mass transfer in microfluidic solvent extraction: part II. Heterogeneous transport with chemical reaction, MICROFLUIDICS AND NANOFLUIDICS, Vol: 14, Pages: 213-224, ISSN: 1613-4982
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- Citations: 25
Mason LR, Ciceri D, Harvie DJE, et al., 2013, Modelling of interfacial mass transfer in microfluidic solvent extraction: part I. Heterogenous transport, MICROFLUIDICS AND NANOFLUIDICS, Vol: 14, Pages: 197-212, ISSN: 1613-4982
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- Citations: 30
Hilton JE, Mason LR, Cleary PW, 2010, Dynamics of gas-solid fluidised beds with non-spherical particle geometry, CHEMICAL ENGINEERING SCIENCE, Vol: 65, Pages: 1584-1596, ISSN: 0009-2509
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- Citations: 152
Mason LR, Reynolds AB, 1998, Comparison of oxidation induction time measurements with values derived from oxidation induction temperature measurements for EPDM and XLPE polymers, POLYMER ENGINEERING AND SCIENCE, Vol: 38, Pages: 1149-1153, ISSN: 0032-3888
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- Citations: 11
REYNOLDS AB, BELL RM, BRYSON NMN, et al., 1995, DOSE-RATE EFFECTS ON THE RADIATION-INDUCED OXIDATION OF ELECTRIC CABLE USED IN NUCLEAR-POWER-PLANTS, RADIATION PHYSICS AND CHEMISTRY, Vol: 45, Pages: 103-110, ISSN: 0146-5724
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- Citations: 14
MASON LR, DOYLE TE, REYNOLDS AB, 1992, EFFECT OF ANTIOXIDANT CONCENTRATION AND RADIATION-DOSE ON OXIDATION INDUCTION TIME, INTERNATIONAL SYMP ON ELECTRICAL INSULATION, Publisher: I E E E, Pages: 169-172
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