16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes

PrefaceThe 16th International Conference on the Modelling of Casting, Welding, and Advanced Solidification Processes (MCWASP XVI) was held from June 18 to 23, 2023, in Banff, Canada, at the Banff Centre for Arts and Creativity. Founded in 1933, the Centre in Treaty 7 Territory within Banff National Park—Canada's first National Park—is a learning organization built upon an extraordinary legacy of excellence in artistic and creative development. The "all-inclusive” nature of the conference and the remote setting meant that participants dined, attended oral and poster presentations, and participated in social activities as a group, fostering outstanding opportunities for networking.Given that the MCWASP community had not met in person since 2015 in Japan (the 2020 edition of MCWASP was virtual owing to COVID-19), the 2023 conference provided the opportunity to renew old friendships and make new ones as well as discuss the science of solidification and related processes—all within the backdrop of the beautiful Canadian Rocky Mountains.The technical program comprised more than 70 oral and poster presentations. In addition to content related to modelling of casting, welding, and advanced solidification processes, keynotes were invited to talk about related subjects (artificial intelligence/machine learning, and permeability modelling in shale rock) as well as the rich diversity of fossils, especially dinosaurs, found in Alberta.The oral technical program was organized with as a single session (i.e., no concurrent presentations). It featured all aspects of solidification modelling, including solidification process technologies (continuous and semi-continuous casting, shape casting, additive manufacturing, and welding), coupled multi-physics simulations, defect formation, fluid flow, micro- and macro-structure formation, numerical methods, and related experimentation, especially in-situ observation of solidification.The four-day technical program was spread over five days to give participants the opportunity to explore the stunning Canadian Rocky Mountains.In these proceedings, the papers are organized by major theme. The dominant topics are Additive Manufacturing and Welding and Microstructure Formation, followed by Continuous Casting – Shape Casting, Heat Transfer and Fluid Flow, Alloy Segregation, Defects, Imaging of Solidification, Thermomechanics, and Materials Properties. In these themes, the authors report advances in numerical modelling techniques, new scientific and process developments in solidification, and related in-situ experimentation.Although significant progress has been made over these past 16 MCWASP conferences covering 43 years, it is clear that the complexity of advanced solidification phenomena as related to conventional and emerging manufacturing technologies still attracts a great deal of scientific and industrial interest to support technological innovation.André PhillionBanff, Canada, June 2023MCWASP XVI 2023List of Peer Reviewers, Sponsors, MCWASP XVI Organizers, International Scientific Committee are available in this Pdf.

Conceptual Design of a UVC-LED Air Purifier to Reduce Airborne Pathogen Transmission—A Feasibility Study

Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a novel UVC-LED (light-emitting diode) air purifier for a low-cost solution to mitigate airborne diseases is proposed. The concept focuses on performance and robustness. It contains a dust-filter assembly, an innovative UVC chamber, and a fan. The low-cost dust filter aims to suppress dust accumulation in the UVC chamber to ensure durability and is conceptually shown to be easily replaced while mitigating any possible contamination. The chamber includes novel turbulence-generating grids and a novel LED arrangement. The turbulent generator promotes air mixing, while the LEDs inactivate the pathogens at a high flow rate and sufficient kill rate. The conceptual design is portable and can fit into ventilation ducts. Computational fluid dynamics and UVC ray methods were used for analysis. The design produces a kill rate above 97% for COVID and tuberculosis and above 92% for influenza A at a flow rate of 100 L/s and power consumption of less than 300 W. An analysis of the dust-filter performance yields the irradiation and flow fields.

Analysis of Fluid Flow on the N95 Facepiece Filtration Layers

Due to the general confusion over the mask's efficiency against COVID-19 virus droplets, professionals and the public may find it difficult to stop the disease from spreading. This study intends to investigate the fluid flow across the N95 filtration layer based on the experimental data and create a simulation model based on the analysis. Therefore, the fluid flow across the filtration layer is simulated using ANSYS Fluent, part of Computational Fluid Dynamics (CFD) solver software. This study anticipates the fluid flow across the N95 by using the porous media approach, in which inertial resistance and viscous resistance are introduced in the simulation by obtaining the pressure drop coefficient of the actual 3M N95 filtration. The fluid passed through the filtration layer with some velocity range based on the MS 2323:2010 standard. The pressure and velocity distribution of fluid through the filtration layer are analyzed by simulation illustration of the contour and streamline of the fluid. For most conditions, as fluid flowed across the filter, the flow gradually began to retard and diverge around the filtration layer. Besides, different flow rates across the filtration layer also result in different pressure distributions on the N95 layer. To sum up, this study is beneficial in forecasting the fluid behavior across the filtration layer by applying the porous zone approach in the ANSYS simulation. © 2022,Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. All rights reserved.

IND04-01 Modernizing drug discovery & development with organ-chip technology

There is no doubt that scientific progress has accelerated the discovery and development of innovative medicines, a phenomenon acutely visible through the rapid advancement of vaccines against SARS-CoV-2. Outside of dealing with a global pandemic, the process of drug discovery and development remains painfully slow, extremely costly and can, despite appropriate measures, result in patient-safety concerns. Because only around 12% of drugs that enter clinical trials make it to approval, governments in the United States and Europe are taking steps towards modernizing the process of drug discovery and development. Whilst several solutions will ultimately be required, there are growing calls for the utilization of 21st century tools within drug discovery pipelines. One such tool is organ-on-a-chip technology that employs microfluidic systems engineering to recapitulate in vivo cell and tissue microenvironments in an organ-specific context. This is achieved by recreating tissue-tissue interfaces and providing fine control over fluid flow and mechanical forces, optionally including supporting interactions with immune cells and microbiome, and reproducing clinical drug exposure profiles. This seminar will showcase the Emulate Organ-Chip platform and will present the findings of the first of its kind Organ-Chip study which utilized the pharma consortium Innovation and Quality (IQ) roadmap for developing in vitro liver models for the prediction of drug-induced liver injury1. Using 780 Liver-Chips across a test set of 27 small molecule drugs, data will be presented indicating that the Liver-Chip has a 87% sensitivity and 100% specificity, thus making it a highly predictive tool compared to animal models and prior preclinical in vitro models. The seminar will complete with an overview on how such a tool can be implemented into drug discovery workflows whilst providing adopting organizations a significant productivity gain.

GLOMERULAR HYPERFILTRATION MAY CONTRIBUTE TO RENAL TROPISM AND KIDNEY INJURY IN COVID-19 BY UPREGULATING ACE2 IN HUMAN PODOCYTES

Objective: Apart from the respiratory system, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can potentially infect multiple other organs including podocytes in the kidney. The latter play a crucial role in glomerular filtration. Podocytes can be damaged by increased fluid flow shear stress (FFSS) of the ultrafiltrate in Bowman's space in the setting of glomerular hyperfiltration that occurs in disease states such as hypertension, diabetes or in several forms of chronic kidney disease. These conditions are associated with an increased risk of a more severe course of coronavirus disease 2019 (COVID-19) and mortality. Design and method: To assess the susceptibility of human podocytes (hPC) for SARS-CoV-2 infection in the context of hyperfiltration in vitro, we used a recently established model system (Streamer Shear Stress Device)) to mimic hyperfiltration by exposing hPC to increased FFSS of 1 dyne/cm2 for 2 h. In this setting we nalysed the effects of FFSS on mRNA expression of angiotensin I-converting enzyme 2 (ACE2) as the pivotal entry receptor for SARS-CoV-2 infection in hPC. Moreover, other potential critical host cell factors including transmembrane serine protease 2 (TMPRSS2), furin (FURIN), and neuropilin 1 (NRP1) were also assessed in parallel with changes of the F-actin fiber structure, i.e. an important cytoskeletal marker in hPC. Results: Under control conditions, hPC displayed long, parallel F-actin fibers crossing the entire cell body. After FFSS, an enrichment of cells that express F-actin in a cortically condensed pattern near the cell membrane was observed. FFSS induced a significant upregulation of ACE2 expression (about twofold) and of all other nalysed SARS-CoV-2 entry factors in hPC (p < 0.05, respectively compared to control conditions, Figure 1 with data plotted as log2fold change [FC]). Conclusions: Our data support a potential link between glomerular hyperfiltration, podocyte damage and renal tropism of SARS-CoV-2 that may contribute to kidney damage including albuminuria development in COVID-19 patients.

W.A.T.E.R. – a structured approach for training on advanced measurement and experimental research

W.A.T.E.R. stands for Workshop on Advanced measurement Techniques and Experimental Research. It is an initiative started in 2016 in association with the Experimental Methods and Instrumentation (EMI) committee of the International Association for Hydro-Environment Engineering and Research (IAHR) aimed at advancing the use of experimental techniques in hydraulics and fluid mechanics research. It provides a structured approach for the learning and training workshop series to postgraduate students (aiming specifically at doctoral students), young researchers, and professionals with an experimental background in fluid mechanics. It offers an opportunity to learn about state-of-the-art instrumentation and measurement techniques and the latest developments in the field by partnering with manufacturers. W.A.T.E.R. brings together academics, instrumentation manufacturers and public sectors in a structured setting to share knowledge and to learn from good practices. It is about training people who already have a certain knowledge and skill level but who need to go deeper and/or wider in the field of measurement and experimental research.

Multiphase large-eddy simulations of human cough jet development and expiratory droplet dispersion

Violent respiratory events play critical roles in the transmission of respiratory diseases, such as coughing and sneezing, between infectious and susceptible individuals. In this work, large-scale multiphase flow large-eddy simulations have been performed to simulate the coughing jet from a human's mouth carrying pathogenic or virus-laden droplets by using a weakly compressible smoothed particle hydrodynamics method. We explicitly model the cough jet ejected from a human mouth in the form of a mixture of two-phase fluids based on the cough velocity profile of the exhalation flow obtained from experimental data and the statistics of the droplets’ sizes. The coupling and interaction between the two expiratory phases and ambient surrounding air are examined based on the interaction between the gas particles and droplet particles. First, the results reveal that the turbulence of the cough jet determines the dispersion of the virus-laden droplets, i.e. whether they fly up evolving into aerosols or fall down to the ground. Second, the droplet particles have significant effects on the evolution of the cough jet turbulence;for example, they increase the complexity and butterfly effect introduced by the turbulence disturbance. Our results show that the prediction of the spreading distance of droplet particles often goes beyond the social distancing rules recommended by the World Health Organization, which reminds us of the risks of exposure if we do not take any protecting protocol.

Application of Machine Learning to Predict the Performance of an EMIPG Reactor Using Data from Numerical Simulations

Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional microwave-driven plasma gasification reactor was developed in ANSYS (Ansys, Canonsburg, PA, USA) Fluent (a CFD tool), to create 644 (geometry and temperature) datasets for training six machine-learning (ML) models. When fed with just geometry datasets, these ML models were able to predict the proportion of the reactor area with temperature above 2000 K. This temperature level is considered a benchmark to prevent formation of undesirable byproducts. The ML model that achieved highest prediction accuracy was the feed forward neural network;the mean absolute error was 0.011. This novel machine-learning model can enable future optimization of experimental microwave plasma gasification systems for application in waste-to-energy.

The Operation of a Three-Bladed Horizontal Axis Wind Turbine under Hailstorm Conditions—A Computational Study Focused on Aerodynamic Performance

The aim of this study is the aerodynamic degradation of a three-bladed Horizontal Axis Wind Turbine (HAWT) under the influence of a hailstorm. The importance and originality of this study are that it explores the aerodynamic performance of an optimum wind turbine blade during a hailstorm, when hailstones and raindrops are present. The commercial Computational Fluid Dynamics (CFD) code ANSYS Fluent 16.0 was utilized for the simulation. The first step was the calculation of the optimum blade geometry characteristics for a three-bladed rotor, i.e., twist and chord length along the blade, by a user-friendly application. Afterwards, the three-dimensional blade and the flow field domain were designed and meshed appropriately. The rotary motion of the blades was accomplished by the application of the Moving Reference Frame Model and the simulation of hailstorm conditions by the Discrete Phase Model. The SST k–ω turbulence model was also added. The produced power of the wind turbine, operating in various environmental conditions, was estimated and discussed. Contours of pressure, hailstone and raindrop concentration and erosion rate, on both sides of the blade, are presented. Moreover, contours of velocity at various cross sections parallel to the rotor are demonstrated, to understand the effect of hailstorms on the wake behavior. The results suggest that the aerodynamic performance of a HAWT degrades due to impact and breakup of the particles on the blade.

Generalized Mittag-Leffler Kernel Form Solutions of Free Convection Heat and Mass Transfer Flow of Maxwell Fluid with Newtonian Heating: Prabhakar Fractional Derivative Approach

In this article, the effects of Newtonian heating along with wall slip condition on temperature is critically examined on unsteady magnetohydrodynamic (MHD) flows of Prabhakar-like non integer Maxwell fluid near an infinitely vertical plate under constant concentration. For the sake of generalized memory effects, a new mathematical fractional model is formulated based on a newly introduced Prabhakar fractional operator with generalized Fourier’s law and Fick’s law. This fractional model has been solved analytically and exact solutions for dimensionless velocity, concentration, and energy equations are calculated in terms of Mittag-Leffler functions by employing the Laplace transformation method. Physical impacts of different parameters such as α, Pr, β, Sc, Gr, γ, and Gm are studied and demonstrated graphically by Mathcad software. Furthermore, to validate our current results, some limiting models such as classical Maxwell model, classical Newtonian model, and fractional Newtonian model are recovered from Prabhakar fractional Maxwell fluid. Moreover, we compare the results between Maxwell and Newtonian fluids for both fractional and classical cases with and without slip conditions, showing that the movement of the Maxwell fluid is faster than viscous fluid. Additionally, it is visualized that both classical Maxwell and viscous fluid have relatively higher velocity as compared to fractional Maxwell and viscous fluid.

Virus spreading and heat spreading

Here we propose a heat transfer framework for how human-to-human interaction spreads everything on the landscape: disease, goods, knowledge, news, technology, science, language and culture. We show that the phenomenon of “human spreading” shares key features with phenomena that are fundamental in physics (heat, electricity, species, Darcy fluid flow), which spread through continua. As example for discussion and illustration, we construct this theoretical framework by using the early phase of the coronavirus outbreak, from before May 2020. The human spreading phenomenon (S curve) is unveiled systematically by using a minimum of measurable parameters: the number of persons with whom one person comes in contact, the radial size of each step in the growth of the swept territory, the radial scale of the inhabited territory, and the directions in which infrastructure (e.g., air routes) are available for long and fast spreading. The resulting configuration of spreading is a multiscale assembly of clusters of fast channels embedded in interstices with slow diffusion. The configuration is dendritic, where each direction of long and fast spreading is covered by a finger of clusters, and each finger generates its own ramifications. The similarities between this configuration and the dendritic architectures for heat and fluid flow through heterogeneous media are discussed.

Duality Between Coronavirus Transmission and Air-Based Macroscopic Molecular Communication.

This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a well-fitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.

Aerosol and bioaerosol particle size and dynamics from defective sanitary plumbing systems.

Aerosols are readily transported on airstreams through building sanitary plumbing and sewer systems, and those containing microbial pathogens (known as bioaerosols) are recognized as contributors to infection spread within buildings. When a defect occurs in the sanitary plumbing system that affects the system integrity, a cross-transmission route is created that can enable the emission of bioaerosols from the system into the building. These emission occurrences are characterized as short-burst events (typically <1 min in duration) which make them difficult to detect and predict. The characterization of these emission events is the focus of this research. Two methods were used to characterize bioaerosol emission events in a full-scale test rig: (a) an Aerodynamic Particle Sizer (APS) for particle size distribution and concentrations; and (b) a slit-to-agar sampler to enumerate the ingress of a viable tracer microorganism (Pseudomonas putida). The APS data confirmed that most particles (>99.5%) were <5 µm and were therefore considered aerosols. Particles generated within the sanitary plumbing system as a result of a toilet flush leads to emissions into the building during system defect conditions with an equivalence of someone talking loudly for over 6 and a half minutes. There were no particles detected of a size >11 µm anywhere in the system. Particle count was influenced by toilet flush volume, but it was not possible to determine if there was any direct influence from airflow rate since both particle and biological data showed no correlation with upward airflow rates and velocities. Typical emissions resulting from a 6 L toilet flush were in the range of 280-400 particles per second at a concentration of typically 9-12 number per cm3 and a total particle count in the region of 3000 to 4000 particles, whereas the peak emissions from a 1.2 L toilet flush were 60-80 particles per second at a concentration of 2.4-3 number per cm3 and a total particle count in the region of 886 to 1045 particles. The reduction in particles is in direct proportion to the reduction in toilet flush volume. The slit-to-agar sampler was able to provide viable time course CFU data and confirmed the origin of the particles to be the tracer microorganism flushed into the system. The time course data also have characteristics consistent with the unsteady nature of a toilet flush.