Multiscale Computational Fluid Dynamics

Modelling Photocatalytic N2 Reduction to Ammonia: Where We Stand and Where We Are Going

Artificial ammonia synthesis via the Haber-Bosch process is environmentally problematic due to the high energy consumption and corresponding CO2 ${_2 }$ emissions, produced during the reaction and before hand in hydrogen production upon methane steam reforming. Photocatalytic nitrogen fixation as a greener alternative to the conventional Haber-Bosch process enables us to perform nitrogen reduction reaction (NRR) under mild conditions, harnessing light as the energy source. Herein, we systematically review first-principles calculations used to determine the electronic/optical properties of photocatalysts, N2 adsorption and to expound possible NRR mechanisms. The most commonly studied photocatalysts for nitrogen fixation are usually modified with dopants, defects, co-catalysts and Z-scheme heterojunctions to prevent charge carrier recombination, improve charge separation efficiency and adjust a band gap to for utilizing a broader light spectrum. Most studies at the atomistic level of modeling are grounded upon density functional theory (DFT) calculations, wholly foregoing excitation effects paramount in photocatalysis. Hence, there is a dire need to consider methods beyond DFT to study the excited state properties more accurately. Furthermore, a few studies have been examined, which include higher level kinetics and macroscale simulations. Ultimately, we show there is still ample room for improvement with regard to first principles calculations and their integration in multiscale models.

A review of the recent progress in biotrickling filters: packing materials, gases, micro-organisms, and CFD

Organic pollutants in the air have serious consequences on both human health and the environment. Among the various methods for removing organic pollution gas, biotrickling filters (BTFs) are becoming more and more popular due to their cost-effective advantages. BTF can effectively degrade organic pollutants without producing secondary pollutants. In the current research on the removal of organic pollutants by BTF, improving the performance of BTF has always been a research hotspot. Researchers have conducted studies from different aspects to improve the removal performance of BTF for organic pollutants. Including research on the performance of BTF using different packing materials, research on the removal of various mixed pollutant gases by BTF, research on microbial communities in BTF, and other studies that can improve the performance of BTF. Moreover, computational fluid dynamics (CFD) was introduced to study the microscopic process of BTF removal of organic pollutants. CFD is a simulation tool widely used in aerospace, automotive, and industrial production. In the study of BTF removal of organic pollutants, CFD can simulate the fluid movement, mass transfer process, and biodegradation process in BTF in a visual way. This review will summarize the development of BTFs from four aspects: packing materials, mixed gases, micro-organisms, and CFD, in order to provide a reference and direction for the future optimization of BTFs.

Visualization of Self-Assembly and Hydration of a β-Hairpin through Integrated Small and Wide-Angle Neutron Scattering

Fundamental understanding of the structure and assembly of nanoscale building blocks is crucial for the development of novel biomaterials with defined architectures and function. However, accessing self-consistent structural information across multiple length scales is challenging. This limits opportunities to exploit atomic scale interactions to achieve emergent macroscale properties. In this work we present an integrative small- and wide-angle neutron scattering approach coupled with computational modeling to reveal the multiscale structure of hierarchically self-assembled β hairpins in aqueous solution across 4 orders of magnitude in length scale from 0.1 Å to 300 nm. Our results demonstrate the power of this self-consistent cross-length scale approach and allows us to model both the large-scale self-assembly and small-scale hairpin hydration of the model β hairpin CLN025. Using this combination of techniques, we map the hydrophobic/hydrophilic character of this model self-assembled biomolecular surface with atomic resolution. These results have important implications for the multiscale investigation of aqueous peptides and proteins, for the prediction of ligand binding and molecular associations for drug design, and for understanding the self-assembly of peptides and proteins for functional biomaterials.

Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system

BACKGROUND AND OBJECTIVES

The use of machine learning methods for modelling bio-systems is becoming prominent which can further improve bio-medical technologies. Physics-informed neural networks (PINNs) can embed the knowledge of physical laws that govern a system during the model training process. PINNs utilise differential equations in the model which traditionally used numerical methods that are computationally complex.

METHODS

We integrate PINNs with an entangled ladder network for modelling respiratory systems by considering a lungs conduction zone to evaluate the respiratory impedance for different initial conditions. We evaluate the respiratory impedance for the inhalation phase of breathing for a symmetric model of the human lungs using entanglement and continued fractions.

RESULTS

We obtain the impedance of the conduction zone of the lungs pulmonary airways using PINNs for nine different combinations of velocity and pressure of inhalation. We compare the results from PINNs with the finite element method using the mean absolute error and root mean square error. The results show that the impedance obtained with PINNs contrasts with the conventional forced oscillation test used for deducing the respiratory impedance. The results show similarity with the impedance plots for different respiratory diseases.

CONCLUSION

We find a decrease in impedance when the velocity of breathing is lowered gradually by 20%. Hence, the methodology can be used to design smart ventilators to the improve flow of breathing.

Deriving force fields with a multiscale approach:from ab initio calculations to molecular-based equations of state

Using theoretical and computational tools for predicting thermophysical properties of fluid systems and the soft matter has always been of interest to the physical, chemical, and engineering sciences. And certainly, the ultimate goal is to be able to compute these macroscopic properties from first principle calculations beginning with the very atomic constitution of matter. In this work, Mie potential parameters were obtained through dimer interaction energy curves derived from ab initio calculations to represent methane and methane-substituted molecules in a spherical 1-site coarse-grained model. Bottom-up-based Mie potential parameters of this work were compared to top-down-based ones from the statistical associating fluid theory (SAFT) models for the calculation of thermodynamic properties and critical point by molecular dynamics simulations and SAFT-VR Mie equation of state. Results demonstrated that bottom-up-based Mie potential parameters when averaging the Mie potential parameters of a representative population of conformers provide values close to the top-down-based ones from SAFT models and predict well properties of tetrahedral molecules. This shows the level of consistency embedded in the SAFT-VR Mie family of models and confers a status of a purely predictive equation of state for SAFT-VR Mie when a reasonable model is considered to represent a molecule of interest.

Numerical Investigation of Flow and Heat Transfer in Rectangular Microchannels with and without Semi-Elliptical Protrusions

Micro-cooling is a growing trend in the field of turbine blade cooling. Technical difficulties in the experiments of large-aspect-ratio rectangular microchannels that are commonly used in the turbine blades cause the rareness of related literature. In this study, the flow characteristics and heat transfer performance of the microchannels with and without semi-ellipsoidal protrusions, whose height is 0.6 mm and width is 9 mm, are numerically investigated. In the microchannel without protrusions, when 2214 < Re < 3589, the velocity has a Λ-shaped distribution, resulting in a Λ-shaped Nu distribution on the wall. When Re > 3760, it is worth noting that from the sidewall to the middle of the channel, Nu first decreases and then increases. In the microchannel with protrusions, when Re < 1214, the turbulence formed by the protrusion is almost all behind it and does not spread to both sides. When 1214 < Re < 2374, the turbulence caused by the protrusions gradually spreads to the middle and both sides of the channel with the increase in Re. When 2374 < Re < 3815, the turbulence caused by two columns of protrusions meet in the middle of the channel and forms stronger turbulence downstream. When Re > 3815, the flow is all turbulent. The protrusions enhance the irreversibility of heat transfer and friction. The performance evaluation criteria (PEC) increases first and then decreases with Re and the maximum value is 1.80 at Re = 2004. In this work, the details that are difficult to obtain in experiments are fully analyzed to provide suggestions for the design of micro-cooling structures in gas turbine blades.

Connecting Gas-Phase Computational Chemistry to Condensed Phase Kinetic Modeling: The State-of-the-Art

In recent decades, quantum chemical calculations (QCC) have increased in accuracy, not only providing the ranking of chemical reactivities and energy barriers (e.g., for optimal selectivities) but also delivering more reliable equilibrium and (intrinsic/chemical) rate coefficients. This increased reliability of kinetic parameters is relevant to support the predictive character of kinetic modeling studies that are addressing actual concentration changes during chemical processes, taking into account competitive reactions and mixing heterogeneities. In the present contribution, guidelines are formulated on how to bridge the fields of computational chemistry and chemical kinetics. It is explained how condensed phase systems can be described based on conventional gas phase computational chemistry calculations. Case studies are included on polymerization kinetics, considering free and controlled radical polymerization, ionic polymerization, and polymer degradation. It is also illustrated how QCC can be directly linked to material properties.

Modelling of Powder Removal for Additive Manufacture Postprocessing

A critical challenge underpinning the adoption of Additive Manufacture (AM) as a technology is the postprocessing of manufactured components. For Powder Bed Fusion (PBF), this can involve the removal of powder from the interior of the component, often by vibrating the component to fluidise the powder to encourage drainage. In this paper, we develop and validate a computational model of the flow of metal powder suitable for predicting powder removal from such AM components. The model is a continuum Eulerian multiphase model of the powder including models for the granular temperature; the effect of vibration can be included through appropriate wall boundaries for this granular temperature. We validate the individual sub-models appropriate for AM metal powders by comparison with in-house and literature experimental results, and then apply the full model to a more complex geometry typical of an AM Heat Exchanger. The model is shown to provide valuable and accurate results at a fraction of the computational cost of a particle-based model.

CFD Simulations of Allothermal Steam Gasification Process for Hydrogen Production

The article presents an experimental laboratory setup used for the empirical determination of the gasification of coal samples in the form of solid rock, cut out in the form of a cylinder. An experimental laboratory set enabled a series of experiments carried out at 700 °C with steam as the gasification agent. The samples were prepared from the coal seam, the use of which can be planned in future underground and ground gasification experiments. The result of the conducted coal gasification process, using steam as the gasification agent, was the syngas, including hydrogen (H2) with a concentration between 46% and 58%, carbon dioxide (CO2) with a concentration between 13% and 17%, carbon monoxide (CO) with a concentration between 7% and 11.5%, and methane(CH4) with a concentration between 9.6% and 20.1%.The results from the ex-situ experiments were compared with the results of numerical simulations using computational fluid dynamics (CFD) methods. A three-dimensional numerical model for the coal gasification process was developed using Ansys-Fluent software to simulate an ex-situ allothermal coal gasification experiment using low-moisture content hard coal under atmospheric conditions. In the numerical model, the mass exchange (flow of the gasification agent), the turbulence description model, heat exchange, the method of simulating the chemical reactions, and the method of mapping the porosity medium were included. Using the construction data of an experimental laboratory set, a numerical model was developed and its discretization (development of a numerical grid, based on which calculations are made) was carried out. Tip on the reactor, supply method, and parameters maintained during the gasification process were used to define the numerical model in the Ansys-Fluent code. A part of the data were supplemented on the basis of literature sources. Where necessary, the literature parameters were converted to the conditions corresponding to the experiment, which were carried out. After performing the calculations, the obtained results were compared with the available experimental data. The experimental and the simulated results were in good agreement, showing a similar tendency.

Impact of Chemistry–Turbulence Interaction Modeling Approach on the CFD Simulations of Entrained Flow Coal Gasification

This paper examines the impact of different chemistry–turbulence interaction approaches on the accuracy of simulations of coal gasification in entrained flow reactors. Infinitely fast chemistry is compared with the eddy dissipation concept considering the influence of turbulence on chemical reactions. Additionally, ideal plug flow reactor study and perfectly stirred reactor study are carried out to estimate the accuracy of chosen simplified chemical kinetic schemes in comparison with two detailed mechanisms. The most accurate global approach and the detailed one are further implemented in the computational fluid dynamics (CFD) code. Special attention is paid to the water–gas shift reaction, which is found to have the key impact on the final gas composition. Three different reactors are examined: a pilot-scale Mitsubishi Heavy Industries reactor, a laboratory-scale reactor at Brigham Young University and a Conoco-Philips E-gas reactor. The aim of this research was to assess the impact of gas phase reaction model accuracy on simulations of the entrained flow gasification process. The investigation covers the following issues: impact of the choice of gas phase kinetic reactions mechanism as well as influence of the turbulence–chemistry interaction model. The advanced turbulence–chemistry models with the complex kinetic mechanisms showed the best agreement with the experimental data.

Recent advances in multiscale CFD modelling of cooling processes and systems for the agrifood industry

Spoilage of agrifood produce is a major issue in the industry. Cooling is an effective technique for extending the shelf life of fresh agrifood produce to minimize spoilage. Due to the practical inability of directly solving the wide spatial and temporal scales in large industrial agrifood cooling systems, the porous medium approach is mostly used. However, improvements of current porous medium models and modeling across much wider scales are needed to better understand the multiscale cooling process and system problems. Recently, as a result of increased computational capacity, multiscale computational fluid dynamics (CFD) modeling approaches have been developed to tackle some of these challenges. The associated problems and applications of CFD in the design and process optimization of cooling processes and systems at different scales are considered. CFD solution and scale bridging techniques relevant for handling multiscale cooling processes and systems problems are discussed. Innovative applications of various CFD modeling techniques at different scales in cooling processes and systems are reviewed. CFD modeling techniques can be used to handle multiscale cooling process and system problems. Lattice Boltzmann method (LBM) is a potentially viable discrete modeling technique for complimentary usages alongside current continuum techniques in future multiscale CFD modeling. The multiscale CFD modeling paradigm can overcome the computational resource limitations associated with the direct modeling approach and enhance model extension across wider spatial and temporal scales. Information from multiscale CFD could be used to improve the accuracy of current porous medium models, and thus the design of more efficient cooling systems.

Multiscale Eulerian CFD of Chemical Processes: A Review

This review covers the scope of multiscale computational fluid dynamics (CFD), laying the framework for studying hydrodynamics with and without chemical reactions in single and multiple phases regarded as continuum fluids. The molecular, coarse-grained particle, and meso-scale dynamics at the individual scale are excluded in this review. Scoping single-scale Eulerian CFD approaches, the necessity of multiscale CFD is highlighted. First, the Eulerian CFD theory, including the governing and turbulence equations, is described for single and multiple phases. The Reynolds-averaged Navier–Stokes (RANS)-based turbulence model such as the standard k-ε equation is briefly presented, which is commonly used for industrial flow conditions. Following the general CFD theories based on the first-principle laws, a multiscale CFD strategy interacting between micro- and macroscale domains is introduced. Next, the applications of single-scale CFD are presented for chemical and biological processes such as gas distributors, combustors, gas storage tanks, bioreactors, fuel cells, random- and structured-packing columns, gas-liquid bubble columns, and gas-solid and gas-liquid-solid fluidized beds. Several multiscale simulations coupled with Eulerian CFD are reported, focusing on the coupling strategy between two scales. Finally, challenges to multiscale CFD simulations are discussed. The need for experimental validation of CFD results is also presented to lay the groundwork for digital twins supported by CFD. This review culminates in conclusions and perspectives of multiscale CFD.

Machine-Learning Methods for Computational Science and Engineering

The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in scientific computing, as well as in data-mining and processing. In this paper, we provide a review of the state-of-the-art in ML for computational science and engineering. We discuss ways of using ML to speed up or improve the quality of simulation techniques such as computational fluid dynamics, molecular dynamics, and structural analysis. We explore the ability of ML to produce computationally efficient surrogate models of physical applications that circumvent the need for the more expensive simulation techniques entirely. We also discuss how ML can be used to process large amounts of data, using as examples many different scientific fields, such as engineering, medicine, astronomy and computing. Finally, we review how ML has been used to create more realistic and responsive virtual reality applications.