Imperial College London

Professor Omar K. Matar, FREng

Faculty of EngineeringDepartment of Chemical Engineering

Head of Department of Chemical Engineering



+44 (0)20 7594 9618o.matar Website




Mr Avery Kitchens +44 (0)20 7594 6263




305 ACEACE ExtensionSouth Kensington Campus





Publication Type

341 results found

Panda D, Kahouadji L, Tuckerman LS, Shin S, Chergui J, Juric D, Matar OKet al., 2023, Axisymmetric and azimuthal waves on a vibrated sessile drop, Physical Review Fluids, Vol: 8

Journal article

Valdes JP, Kahouadji L, Liang F, Shin S, Chergui J, Juric D, Matar OKet al., 2023, On the dispersion dynamics of liquid–liquid surfactant-laden flows in a SMX static mixer, Chemical Engineering Journal, Vol: 475, ISSN: 1385-8947

This study aims to elucidate, for the first time, the intricate fundamental physics governing the dispersion dynamics of a surfactant-laden two-phase liquid–liquid system in the well-known SMX static mixer. Following the analysis carried out in the preceding publication to this work (Valdes et al., 2023), a comparative assessment of the most relevant and recurrent deformation and breakup mechanisms is conducted for a 3-drop scenario and then extrapolated to a more industrially-relevant multi-drop set-up. A parametric study on relevant surfactant physico-chemical parameters (i.e., elasticity, sorption kinetics) is undertaken, isolating each property by considering insoluble and soluble surfactants. In addition, the role of Marangoni stresses on the deformation and breakage dynamics is explored. High fidelity, three-dimensional direct numerical simulations coupled with a state-of-the-art hybrid interface capturing algorithm are carried out, providing a wealth of information previously inaccessible via volume-averaged or experimental approaches.

Journal article

Savage T, Basha N, McDonough J, Matar OK, del Rio Chanona EAet al., 2023, Multi-fidelity data-driven design and analysis of reactor and tube simulations, Computers and Chemical Engineering, Vol: 179, ISSN: 0098-1354

Optimizing complex reactor geometries is vital to promote enhanced efficiency. We present a framework to solve this nonlinear, computationally expensive, and derivative-free problem. Gaussian processes are used to learn a multi-fidelity model of reactor simulations correlating multiple continuous mesh fidelities. The search space of reactor geometries is explored through lower fidelity simulations, evaluated based on a weighted acquisition function, trading off information gain with cost. Within our framework, DARTS, we derive a novel criteria for dictating optimization termination, ensuring a high fidelity solution is returned before budget is exhausted. We investigate the design of helical-tube reactors under pulsed-flow conditions, which have demonstrated outstanding mixing characteristics. To validate our results, we 3D print and experimentally validate the optimal reactor geometry, confirming mixing performance. Our approach is applicable to a broad variety of expensive simulation-based optimization problems, enabling the design of novel parameterized chemical reactors.

Journal article

Pico P, Nathanael K, Lavino AD, Kovalchuk NM, Simmons MJH, Matar OKet al., 2023, Silver nanoparticles synthesis in microfluidic and well-mixed reactors: A combined and PBM-CFD, CHEMICAL ENGINEERING JOURNAL, Vol: 474, ISSN: 1385-8947

Journal article

Muller E, Lew JH, Matar O, Luckham P, Santos A, Thant MMTet al., 2023, Atomic Force Microscopy of hydrolysed polyacrylamide adsorption onto calcium carbonate, Polymers, ISSN: 2073-4360

Journal article

Traverso T, Abadie T, Matar OK, Magri Let al., 2023, Data-driven modeling for drop size distributions, Physical Review Fluids, Vol: 8

The prediction of the drop size distribution (DSD) resulting from liquid atomization is key to the optimization of multiphase flows from gas-turbine propulsion through agriculture to healthcare. Obtaining high-fidelity data of liquid atomization, either experimentally or numerically, is expensive, which makes the exploration of the design space difficult. First, to tackle these challenges, we propose a framework to predict the DSD of a liquid spray based on data as a function of the spray angle, the Reynolds number, and the Weber number. Second, to guide the design of liquid atomizers, the model accurately predicts the volume of fluid contained in drops of specific sizes while providing uncertainty estimation. To do so, we propose a Gaussian process regression (GPR) model, which infers the DSD and its uncertainty form the knowledge of its integrals and of its first moment, i.e., the mean drop diameter. Third, we deploy multiple GPR models to estimate these quantities at arbitrary points of the design space from data obtained from a large number of numerical simulations of a flat fan spray. The kernel used for reconstructing the DSD incorporates prior physical knowledge, which enables the prediction of sharply peaked and heavy-tailed distributions. Fourth, we compare our method with a benchmark approach, which estimates the DSD by interpolating the frequency polygon of the binned drops with a GPR. We show that our integral approach is significantly more accurate, especially in the tail of the distribution (i.e., large, rare drops), and it reduces the bias of the density estimator by up to 10 times. Finally, we discuss physical aspects of the model's predictions and interpret them against experimental results from the literature. This work opens opportunities for modeling drop size distribution in multiphase flows from data.

Journal article

Basha N, Savage T, McDonough J, del Rio Chanona EA, Matar OKet al., 2023, Discovery of mixing characteristics for enhancing coiled reactor performance through a Bayesian optimisation-CFD approach, Chemical Engineering Journal, Vol: 473, ISSN: 1385-8947

Plug flow characteristics are advantageous in various manufacturing processes for fine/bulk chemicals, pharmaceuticals, biofuels, and waste treatment as they contribute to maximising product yield. 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.

Journal article

Liang F, Kahouadji L, Valdes JP, Shin S, Chergui J, Juric D, Matar OKet al., 2023, Numerical simulation of surfactant-laden emulsion formation in an un-baffled stirred vessel, CHEMICAL ENGINEERING JOURNAL, Vol: 472, ISSN: 1385-8947

Journal article

Hue KY, Lew JH, Myo Thant MM, Matar O, Luckham P, Muller Eet al., 2023, Molecular dynamics simulation of polyacrylamide adsorption on calcite, Molecules, Vol: 28, Pages: 1-17, ISSN: 1420-3049

In poorly consolidated carbonate rock reservoirs, solids production risk, which can lead to increased environmental waste, can be mitigated by injecting formation-strengthening chemicals. Classical atomistic molecular dynamics (MD) simulation is employed to model the interaction of polyacrylamide-based polymer additives with a calcite structure, which is the main component of carbonate formations. Amongst the possible calcite crystal planes employed as surrogates of reservoir rocks, the (1 0 4) plane is shown to be the most suitable surrogate for assessing the interactions with chemicals due to its stability and more realistic representation of carbonate structure. The molecular conformation and binding energies of pure polyacrylamide (PAM), hydrolysed polyacrylamide in neutral form (HPAM), hydrolysed polyacrylamide with 33% charge density (HPAM 33%) and sulfonated polyacrylamide with 33% charge density (SPAM 33%) are assessed to determine the adsorption characteristics onto calcite surfaces. An adsorption-free energy analysis, using an enhanced umbrella sampling method, is applied to evaluate the chemical adsorption performance. The interaction energy analysis shows that the polyacrylamide-based polymers display favourable interactions with the calcite structure. This is attributed to the electrostatic attraction between the amide and carboxyl functional groups with the calcite. Simulations confirm that HPAM33% has a lower free energy than other polymers, presumably due to the presence of the acrylate monomer in ionised form. The superior chemical adsorption performance of HPAM33% agrees with Atomic Force Microscopy experiments reported herein.

Journal article

Zhu K, Cheng S, Kovalchuk N, Simmons M, Guo Y-K, Matar OK, Arcucci Ret al., 2023, Analyzing drop coalescence in microfluidic devices with a deep learning generative model, Physical Chemistry Chemical Physics, Vol: 25, Pages: 15744-15755, ISSN: 1463-9076

Predicting drop coalescence based on process parameters is crucial for experimental design in chemical engineering. However, predictive models can suffer from the lack of training data and more importantly, the label imbalance problem. In this study, we propose the use of deep learning generative models to tackle this bottleneck by training the predictive models using generated synthetic data. A novel generative model, named double space conditional variational autoencoder (DSCVAE) is developed for labelled tabular data. By introducing label constraints in both the latent and the original space, DSCVAE is capable of generating consistent and realistic samples compared to the standard conditional variational autoencoder (CVAE). Two predictive models, namely random forest and gradient boosting classifiers, are enhanced on synthetic data and their performances are evaluated based on real experimental data. Numerical results show that a considerable improvement in prediction accuracy can be achieved by using synthetic data and the proposed DSCVAE clearly outperforms the standard CVAE. This research clearly provides more insights into handling imbalanced data for classification problems, especially in chemical engineering.

Journal article

Constante-Amores CR, Kahouadji L, Shin S, Chergui J, Juric D, Castrejón-Pita JR, Matar OK, Castrejón-Pita AAet al., 2023, Impact of droplets onto surfactant-laden thin liquid films, Journal of Fluid Mechanics, Vol: 961, ISSN: 0022-1120

We study the effect of insoluble surfactants on the impact of surfactant-free droplets onto surfactant-laden thin liquid films via a fully three-dimensional direct numerical simulation approach that employs a hybrid interface-tracking/level-set method, and by taking into account surfactant-induced Marangoni stresses due to gradients in interfacial surfactant concentration. Our numerical predictions for the temporal evolution of the surfactant-free crown are validated against the experimental work by Che & Matar (Langmuir, vol. 33, 2017, pp. 12140–12148). We focus on the ‘crown-splash regime’, and we observe that the crown dynamics evolves through various stages: from the growth of linear modes (through a Rayleigh–Plateau instability) to the development of nonlinearities leading to primary and secondary breakup events (through droplet shedding modulated by an end-pinching mechanism). We show that the addition of surfactants does not affect the wave selection via the Rayleigh–Plateau instability. However, the presence of surfactants plays a key role in the late stages of the dynamics as soon as the ligaments are driven out from the rim. Surfactant-induced Marangoni stresses delay the end-pinching mechanisms to result in longer ligaments prior to their capillary singularity. Our results indicate that Marangoni stresses bridge the gap between adjacent protrusions promoting the adjacent protrusions' collision and the merging of ligaments. Finally, we demonstrate that the addition of surfactants leads to surface rigidification and consequently to the retardation of the flow dynamics.

Journal article

Kalli M, Pico P, Chagot L, Kahouadji L, Shin S, Chergui J, Juric D, Matar OK, Angeli Pet al., 2023, Effect of surfactants during drop formation in a microfluidic channel: a combined experimental and computational fluid dynamics approach, Journal of Fluid Mechanics, Vol: 961, Pages: 1-25, ISSN: 0022-1120

The effect of surfactants on the flow characteristics during rapid drop formation in a microchannel is investigated using high-speed imaging, micro-particle image velocimetry and numerical simulations; the latter are performed using a three- dimensional multiphase solver that accounts for the transport of soluble surfactants in the bulk and at the interface. Drops are generated in a flow-focusing microchannel, using silicone oil ( 4.6 mPa s) as the continuous phase and a 52 % w/w glycerol solution as the dispersed phase. A non-ionic surfactant (Triton X-100) is dissolved in the dispersed phase at concentrations below and above the critical micelle concentration. Good agreement is found between experimental and numerical data for the drop size, drop formation time and circulation patterns. The results reveal strong circulation patterns in the forming drop in the absence of surfactants, whose intensity decreases with increasing surfactant concentration. The surfactant concentration profiles in the bulk and at the interface are shown for all stages of drop formation. The surfactant interfacial concentration is large at the front and the back of the forming drop, while the neck region is almost surfactant free. Marangoni stresses develop away from the neck, contributing to changes in the velocity profile inside the drop.

Journal article

Cooper J, Bird M, Acha S, Amrit P, Chachuat B, Shah N, Matar Oet al., 2023, The Carbon Footprint of a UK Chemical Engineering Department – The Case of Imperial College London, The 30th CIRP Life Cycle Engineering Conference, Publisher: Elsevier, Pages: 444-449, ISSN: 2212-8271

As the UK strives towards net-zero it is important that all sectors, including Higher Education, take immediate measures to cut their greenhouse gas emissions. The greenhouse gases emitted by different Higher Education institutions are studied and are shown to be large. However, these studies are based on aggregated data, and it is therefore uncertain how effective institute-wide policies to cut emissions are at department level. Herein, we present a generic framework for university departments to calculate their carbon footprint considering Scope 1, 2 and 3 emissions. We estimate the carbon footprint of the Chemical Engineering Department at Imperial College London to be 7,620 and 8,330 tCO2eq in 2018/19 and 2019/20, respectively. Scope 3 emissions account for 54% of the Department's emissions with Scope 1 and 2 accounting for the remaining 46%. Scope 3 emissions are largely driven by purchased goods and travel, while Scope 1 emissions are predominantly from electricity usage.

Conference paper

Hennessy MG, Craster RV, Matar OK, 2023, Time-dependent modelling of thin poroelastic films drying on deformable plates, European Journal of Applied Mathematics, Pages: 1-34, ISSN: 0956-7925

Understanding the generation of mechanical stress in drying, particle-laden films is important for a wide range of industrial processes. One way to study these stresses is through the cantilever experiment, whereby a thin film is deposited onto the surface of a thin plate that is clamped at one end to a wall. The stresses that are generated in the film during drying are transmitted to the plate and drive bending. Mathematical modelling enables the film stress to be inferred from measurements of the plate deflection. The aim of this paper is to present simplified models of the cantilever experiment that have been derived from the time-dependent equations of continuum mechanics using asymptotic methods. The film is described using nonlinear poroelasticity and the plate using nonlinear elasticity. In contrast to Stoney-like formulae, the simplified models account for films with non-uniform thickness and stress. The film model reduces to a single differential equation that can be solved independently of the plate equations. The plate model reduces to an extended form of the Föppl-von Kármán (FvK) equations that accounts for gradients in the longitudinal traction acting on the plate surface. Consistent boundary conditions for the FvK equations are derived by resolving the Saint-Venant boundary layers at the free edges of the plate. The asymptotically reduced models are in excellent agreement with finite element solutions of the full governing equations. As the Péclet number increases, the time evolution of the plate deflection changes from t to t1/2 , in agreement with experiments.

Journal article

Valdes JP, Kahouadji L, Liang F, Shin S, Chergui J, Juric D, Matar OKet al., 2023, Direct numerical simulations of liquid–liquid dispersions in a SMX mixer under different inlet conditions, Chemical Engineering Journal, Vol: 462, Pages: 1-18, ISSN: 1385-8947

The internal dynamics of static mixers handling liquid–liquid flows have been comprehensively explored over the past decade. Although the effect of the inlet configuration is often overlooked, a few studies have suggested a relationship between the phases’ initial set-up and the performance of the mixer in terms of the droplet size distribution (DSD). Accordingly, different dispersed phase morphologies at the inlet of a SMX static mixer have been tested and their effect on the overall dispersion performance of the mixer has been evaluated based on the DSD and growth of interfacial area. In particular, three representative scenarios are considered: (1) Isolated cases, where one and three individual droplets are injected, mimicking a controlled syringe injection; (2) Numerous variable-sized droplets, simulating a pre-mixed/dispersed inlet; and (3) Jet inlet, emulating a standard phase injection from a gear pump. In addition, this study provides novel insight into the underlying physics dictating droplet deformation and breakage in SMX mixers for industrially-relevant scenarios. This can be achieved thanks to the massively-parallel high-fidelity three-dimensional direct numerical simulations computed with a robust hybrid front-tracking/level-set algorithm, which provides a wealth of information on intricate interfacial dynamics; this information cannot be obtained via experimental or volume-averaged modelling techniques implemented in past studies.

Journal article

Chen J, Anastasiou C, Cheng S, Basha NM, Kahouadji L, Arcucci R, Angeli P, Matar OKet al., 2023, Computational fluid dynamics simulations of phase separation in dispersed oil-water pipe flows, Chemical Engineering Science, Vol: 267, Pages: 1-18, ISSN: 0009-2509

The separation of liquid–liquid dispersions in horizontal pipes is common in many industrial sectors. It remains challenging, however, to predict the separation characteristics of the flow evolution due to the complex flow mechanisms. In this work, Computational Fluid Dynamics (CFD) simulations of the silicone oil and water two-phase flow in a horizontal pipe are performed. Several cases are explored with different mixture velocities and oil fractions (15%-60%). OpenFOAM (version 8.0) is used to perform Eulerian-Eulerian simulations coupled with population balance models. The ‘blending factor’ in the multiphaseEulerFoam solver captures the retardation of the droplet rising and coalescing due to the complex flow behaviour in the dense packed layer (DPL). The blending treatment provides a feasible compensation mechanism for the mesoscale uncertainties of droplet flow and coalescence through the DPL and its adjacent layers. In addition, the influence of the turbulent dispersion force is also investigated, which can improve the prediction of the radial distribution of concentrations but worsen the separation characteristics along the flow direction. Although the simulated concentration distribution and layer heights agree with the experiments only qualitatively, this work demonstrates how improvements in drag and coalescence modelling can be made to enhance the prediction accuracy.

Journal article

Constante-Amores CR, Abadie T, Kahouadji L, Shin S, Chergui J, Juric D, Castrejon-Pita AA, Matar OKet al., 2023, Direct numerical simulations of turbulent jets: vortex-interface-surfactant interactions, Journal of Fluid Mechanics, Vol: 955, Pages: 1-25, ISSN: 0022-1120

We study the effect of insoluble surfactants on the spatio-temporal evolution of turbulent jets. We use three-dimensional numerical simulations and employ an interface-tracking/level-set method that accounts for surfactant-induced Marangoni stresses. The present study builds on our previous work (Constante-Amores et al., J. Fluid Mech., vol. 922, 2021, A6) in which we examined in detail the vortex–surface interaction in the absence of surfactants. Numerical solutions are obtained for a wide range of Weber and elasticity numbers in which vorticity production is generated by surface deformation and surfactant-induced Marangoni stresses. The present work demonstrates, for the first time, the crucial role of Marangoni stresses, brought about by surfactant concentration gradients, in the formation of coherent, hairpin-like vortex structures. These structures have a profound influence on the development of the three-dimensional interfacial dynamics. We also present theoretical expressions for the mechanisms that influence the rate of production of circulation in the presence of surfactants for a general, three-dimensional, two-phase flow, and highlight the dominant contribution of surfactant-induced Marangoni stresses.

Journal article

Cheng S, Chen J, Anastasiou C, Angeli P, Matar OKK, Guo Y-K, Pain CCC, Arcucci Ret al., 2023, Generalised latent assimilation in heterogeneous reduced spaces with machine learning surrogate models, Journal of Scientific Computing, Vol: 94, Pages: 1-37, ISSN: 0885-7474

Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.

Journal article

Savage T, Basha N, Matar OK, del Rio Chanona Aet al., 2023, Multi-fidelity Bayesian Optimisation of Reactor Simulations using Deep Gaussian Processes, Computer Aided Chemical Engineering, Pages: 511-517

Coiled tube reactors under pulsed-flow conditions have been shown to provide promising mixing characteristics. In order to validate performance in an industrial setting, and investigate the underlying physics of successful mixing, coiled tube reactors must be optimised. In this work, we apply a novel framework to locate optimal solutions to this nonlinear, computationally expensive, and derivative-free problem. Our optimisation framework takes advantage of deep Gaussian processes to learn a multi-fidelity surrogate model. We apply this model within a novel Bayesian optimisation algorithm, using faster, less accurate, and potentially biased lower-fidelity simulations to enable faster reactor optimisation. We subsequently investigate the physical insights into the swirling flows of these optimal configurations, directly informing the design of future coiled-tube reactors under pulsed-flow conditions. We demonstrate our design framework to be extensible to a broad variety of expensive simulation-based optimisation problems, supporting the design of the next-generation of highly parameterised chemical reactors.

Book chapter

Liang F, Kahouadji L, Valdes JP, Shin S, Chergui J, Juric D, Matar OKet al., 2022, Numerical study of oil–water emulsion formation in stirred vessels: effect of impeller speed, Flow: Applications of Fluid Mechanics, Vol: 2, Pages: 1-19, ISSN: 2633-4259

The mixing of immiscible oil and water by a pitched blade turbine in a cylindrical vessel is studied numerically. Three-dimensional simulations combined with a hybrid front-tracking/level-set method are employed to capture the complex flow and interfacial dynamics. A large eddy simulation approach, with a Lilly–Smagorinsky model, is employed to simulate the turbulent two-phase dynamics at large Reynolds numbers Re=1802−18 026 . The numerical predictions are validated against previous experimental work involving single-drop breakup in a stirred vessel. For small Re , the interface is deformed but does not reach the impeller hub, assuming instead the shape of a Newton's Bucket. As the rotating speed increases, the deforming interface attaches to the impeller hub which leads to the formation of long ligaments that subsequently break up into small droplets. For the largest Re studied, the system dynamics becomes extremely complex wherein the creation of ligaments, their breakup and the coalescence of drops occur simultaneously. The simulation outcomes are presented in terms of spatio-temporal evolution of the interface shape and vortical structures. The results of a drop size analysis in terms of the evolution of the number of drops, and their size distribution, is also presented as a parametric function of Re .

Journal article

Kahouadji L, Liang F, Valdes JP, Shin S, Chergui J, Juric D, Craster RV, Matar OKet al., 2022, The transition to aeration in turbulent two-phase mixing in stirred vessels, Flow, Turbulence and Combustion, Vol: 2, Pages: 1-20, ISSN: 0003-6994

We consider the mixing dynamics of an air–liquid system driven by the rotation of a pitched blade turbine (PBT) inside an open, cylindrical tank. To examine the flow and interfacial dynamics, we use a highly parallelised implementation of a hybrid front-tracking/level-set method that employs a domain-decomposition parallelisation strategy. Our numerical technique is designed to capture faithfully complex interfacial deformation, and changes of topology, including interface rupture and dispersed phase coalescence. As shown via transient, a three-dimensional (3-D) LES (large eddy simulation) using a Smagorinsky–Lilly turbulence model, the impeller induces the formation of primary vortices that arise in many idealised rotating flows as well as several secondary vortical structures resembling Kelvin–Helmholtz, vortex breakdown, blade tip vortices and end-wall corner vortices. As the rotation rate increases, a transition to ‘aeration’ is observed when the interface reaches the rotating blades leading to the entrainment of air bubbles into the viscous fluid and the creation of a bubbly, rotating, free surface flow. The mechanisms underlying the aeration transition are probed as are the routes leading to it, which are shown to exhibit a strong dependence on flow history.

Journal article

Chagot L, Quilodran-Casas C, Kalli M, Kovalchuk NM, Simmons MJH, Matar OK, Arcucci R, Angeli Pet al., 2022, Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach, LAB ON A CHIP, Vol: 22, Pages: 3848-3859, ISSN: 1473-0197

Journal article

Municchi F, El Mellas I, Matar OK, Magnini Met al., 2022, Conjugate heat transfer effects on flow boiling in microchannels, International Journal of Heat and Mass Transfer, Vol: 195, Pages: 123166-123166, ISSN: 0017-9310

This article presents a computational study of saturated flow boiling in non-circular microchannels. The unit channel of a multi-microchannel evaporator, consisting of the fluidic channel and surrounding evaporator walls, is simulated and the conjugate heat transfer problem is solved. Simulations are performed using OpenFOAM v2106 and the built-in geometric Volume Of Fluid method, augmented with self-developed libraries to include liquid-vapour phase-change and improve the surface tension force calculation. A systematic study is conducted by employing water at atmospheric pressure, a channel hydraulic diameter of µm, a uniform base heat flux of , and by varying the channel width-to-height aspect-ratio and channel fin thickness in the range –4 and , respectively. The effects of conjugate heat transfer and channel aspect-ratio on the bubble and evaporative film dynamics, heat transfer, and evaporator temperature are investigated in detail. This study reveals that, when the flow is single-phase, higher Nusselt numbers and lower evaporator base temperatures are achieved for smaller channel aspect-ratios, from and when , to and when , for same fin thickness . In the two-phase flow regime, Nusselt numbers in the range are achieved. The trends of the Nusselt number versus the aspect-ratio are non-monotonic and exhibit a marked dependence on the channel fin thickness. For small fin thicknesses, and , an overall ascending trend of for increasing aspect-ratios is apparent, although in the narrower range –2 the Nusselt number appears weakly dependent on . For thicker fins, and , the Nusselt number decreases slightly when increasing the aspect-ratio in the range –2, although this trend is not monotonic when considering the entire range of aspect-ratios investigated. Nonetheless, due to conjugate heat transfer, Nusselt numbers and evaporator base temperatures follow different trends when varying the aspect-ratio, and channels with seem to promo

Journal article

Gonsalves GFN, Matar OK, 2022, Mechanistic modelling of two-phase slug flows with deposition, Chemical Engineering Science, Vol: 259, ISSN: 0009-2509

Despite the large quantity of works dedicated to the analysis and modelling of deposition in single-phase flows, very few models have been proposed for deposition of solids in two-phase pipe flows of gas and liquid. A comprehensive mechanistic model for transient, multiphase pipe flow with phase change is proposed, which takes into account effects of deposition by mass diffusion, ageing, and shearing of the deposit layer. Validation is performed with an exhaustive database of experimental data from the literature, for steady and transient flows without deposition, and deposit thickness measurements for single-phase, slug and stratified flows. Most of the predictions are within a 20% error band from the experimental data, with slightly worse performance in the case of deposition in slug flows. A surrogate model is also developed based on active-learning sampling of the simulator results, demonstrating a technique that could be used for optimization or design of engineering devices.

Journal article

Zhuang Y, Cheng S, Kovalchuk N, Simmons M, Matar OK, Guo Y-K, Arcucci Ret al., 2022, Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device, Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering, Vol: 22, Pages: 3187-3202, ISSN: 1473-0189

A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.

Journal article

Botsas T, Pan I, Mason LR, Matar OKet 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.

Journal article

Hennessy MG, Craster R, Matar OK, 2022, Drying-induced stresses in poroelastic drops on rigid substrates, Physical Review E, Vol: 105, ISSN: 2470-0045

We develop a theory for drying-induced stresses in sessile, poroelastic drops undergoing evaporation on rigid surfaces. Using a lubrication-like approximation, the governing equations of three-dimensional nonlinear poroelasticity are reduced to a single thin-film equation for the drop thickness. We find that thin drops experience compressive elastic stresses but the total in-plane stresses are tensile. The mechanical response of the drop is dictated by the initial profile of the solid skeleton, which controls the in-plane deformation, the dominant components of elastic stress, and sets a limit on the depth of delamination that can potentially occur. Our theory suggests that the alignment of desiccation fractures in colloidal drops is selected by the shape of the drop at the point of gelation. We propose that the emergence of three distinct fracture patterns in dried blood drops is a consequence of a nonmonotonic drop profile at gelation. We also show that depletion fronts, which separate wet and dry solid, can invade the drop from the contact line and localize the generation of mechanical stress during drying. Finally, the finite element method is used to explore the stress profiles in drops with large contact angles.

Journal article

Nathanael K, Pico P, Kovalchuk NM, Lavino AD, Simmons MJH, Matar OKet al., 2022, Computational modelling and microfluidics as emerging approaches to synthesis of silver nanoparticles – A review, Chemical Engineering Journal, Vol: 436, Pages: 135178-135178, ISSN: 1385-8947

This review provides an integrated overview of the current state of knowledge for sustainable production of silver nanoparticles (AgNPs), focussing on recent advances in their synthesis using emerging microfluidic-based methods and computational modelling, their properties and practical applications. Special attention is given to the Finke-Watzky two-step kinetic model, which provides the best fitting for nucleation and growth of AgNPs and the multiple operating parameters that affect their physical and chemical properties. An overview of numerical simulations used to model the synthesis of AgNPs across different length and time scales is presented. Investigations made at the molecular scale via molecular dynamics (MD) simulations, at the meso- and macroscale via population balance modelling (PBM) and computational fluid dynamics (CFD), respectively are discussed, alongside data-driven modelling approaches. The review also identifies both limitations and advantages in exploiting the aforementioned techniques, offering a way forward for further investigations on the topic. A critical analysis of the literature leads to confirm that the combination of microfluidics-based synthesis, which enable reactions to be carried out under highly-controlled conditions, along with physics-driven simulations and data-driven models are a powerful tool to effectively link input information of the process and output data related to the properties of the AgNPs. This combined framework therefore provides an opportunity to improve the prediction accuracy of the whole cycle of synthesis of AgNPs and overcome the environmental impact and limitations of traditional methods.

Journal article

Heaney CE, Wolffs Z, Tómasson JA, Kahouadji L, Salinas P, Nicolle A, Navon IM, Matar OK, Srinil N, Pain CCet al., 2022, An AI-based non-intrusive reduced-order model for extended domains applied to multiphase flow in pipes, Physics of Fluids, Vol: 34, Pages: 1-22, ISSN: 1070-6631

The modeling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of meters vs a pipe diameter of just a few inches. Approximating CFD models in a low-dimensional space, reduced-order models have been shown to produce accurate results with a speed-up of orders of magnitude. In this paper, we present a new AI-based non-intrusive reduced-order model within a domain decomposition framework (AI-DDNIROM), which is capable of making predictions for domains significantly larger than the domain used in training. This is achieved by (i) using a domain decomposition approach; (ii) using dimensionality reduction to obtain a low-dimensional space in which to approximate the CFD model; (iii) training a neural network to make predictions for a single subdomain; and (iv) using an iteration-by-subdomain technique to converge the solution over the whole domain. To find the low-dimensional space, we compare Proper Orthogonal Decomposition with several types of autoencoder networks, known for their ability to compress information accurately and compactly. The comparison is assessed with two advection-dominated problems: flow past a cylinder and slug flow in a pipe. To make predictions in time, we exploit an adversarial network, which aims to learn the distribution of the training data, in addition to learning the mapping between particular inputs and outputs. This type of network has shown the potential to produce visually realistic outputs. The whole framework is applied to multiphase slug flow in a horizontal pipe for which an AI-DDNIROM is trained on high-fidelity CFD simulations of a pipe of length 10 m with an aspect ratio of 13:1 and tested by simulating the flow for a pipe of length 98 m with an aspect ratio of almost 130:1. Inspection of the predicted liquid volume

Journal article

Abubakar HA, Matar OK, 2022, Linear stability analysis of Taylor bubble motion in downward flowing liquids in vertical tubes, Journal of Fluid Mechanics, Vol: 941, ISSN: 0022-1120

Taylor bubbles are a feature of the slug flow regime in gas–liquid flows in vertical pipes. Their dynamics exhibits a number of transitions such as symmetry breaking in the bubble shape and wake when rising in downward flowing and stagnant liquids, respectively, as well as breakup in sufficiently turbulent environments. Motivated by the need to examine the stability of a Taylor bubble in liquids, a systematic numerical study of a steadily moving Taylor bubble in stagnant and flowing liquids is carried out, characterised by the dimensionless inverse viscosity (Nf), Eötvös (Eo) and Froude numbers (Um), the latter being based on the centreline liquid velocity, using a Galerkin finite-element method. A boundary-fitted domain is used to examine the dependence of the steady bubble shape on a wide range of Nf, Eo and Um. Our analysis of the bubble nose and bottom curvatures shows that the intervals Eo=[20,30) and Nf=[60,80) are the limits below which surface tension and viscosity, respectively, have a strong influence on the bubble shape. In the interval Eo=(60,100], all bubble features studied are weakly dependent on surface tension. A linear stability analysis of the axisymmetric base states shows that there exist regions of (Nf,Eo,Um) space within which the bubble is unstable and assumes an asymmetric shape. To elucidate the mechanisms underlying the instability, an energy budget analysis is carried out which reveals that perturbation growth is driven by the bubble pressure for Eo≥100, and by the tangential interfacial stress for Eo<100. Examples of the asymmetric bubble shapes and their associated flow fields are also provided near the onset of instability for a wide range of Nf, Eo and Um.

Journal article

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: Request URI: /respub/WEB-INF/jsp/search-html.jsp Query String: respub-action=search.html&id=00169227&limit=30&person=true