Comprehensive Computational Model Research Articles

Family Malvaceae: a potential source of secondary metabolites with chemopreventive and anticancer activities supported with in silico pharmacokinetic and pharmacodynamic profiles.

Cancer is the second most widespread cause of mortality following cardiovascular disorders, and it imposes a heavy global burden. Nowadays, herbal nutraceutical products with a plethora of bioactive metabolites represent a foundation stone for the development of promising chemopreventive and anticancer agents. Certain members of the family Malvaceae have traditionally been employed to relieve tumors. The literature concerning the chemopreventive and anticancer effects of the plant species along with the isolated cytotoxic phytometabolites was reviewed. Based on the findings, comprehensive computational modelling studies were performed to explore the pharmacokinetic and pharmacodynamic profiles of the reported cytotoxic metabolites to present basis for future plant-based anticancer drug discovery. All the available information about the anticancer research in family Malvaceae and its cytotoxic phytometabolites were retrieved from official sources. Extensive search was carried out using the keywords Malvaceae, cancer, cytotoxicity, mechanism and signalling pathway. Pharmacokinetic study was performed on the cytotoxic metabolites using SWISS ADME model. Acute oral toxicity expressed as median lethal dose (LD50) was predicted using Pro Tox 3.0 web tool. The compounds were docked using AutoDock Vina platform against epidermal growth factor receptor (EGFR kinase enzyme) obtained from the Protein Data Bank. Molecular dynamic simulations and MMGBSA calculations were performed using GROMACS 2024.2 and gmx_MMPBSA tool v1.5.2. One hundred forty-five articles were eligible in the study. Several tested compounds showed safe pharmacokinetic properties. Also, the molecular docking study showed that the bioactive metabolites possessed agreeable binding affinities to EGFR kinase enzyme. Tiliroside (25), boehmenan (30), boehmenan H (31), and isoquercetin (22) elicited the highest binding affinity toward the enzyme with a score of -10.4, -10.4, -10.2 and -10.1Kcal/mol compared to the reference drug erlotinib having a binding score equal to -9Kcal/mol. Additionally, compounds 25 and 31 elicited binding free energies equal to -42.17 and -42.68Kcal/mol, respectively, comparable to erlotinib. Overall, the current study presents helpful insights into the pharmacokinetic and pharmacodynamic properties of the reported cytotoxic metabolites belonging to family Malvaceae members. The molecular docking and dynamic simulations results intensify the roles of secondary metabolites from medicinal plants in fighting cancer.

Nanozyme-enhanced enzyme control: Computational strategies for precise and sustained enzymatic activity in tissue engineering

Nanomaterials are revolutionizing tissue engineering by significantly enhancing enzyme-mediated chemical processes and cellular activities, thereby advancing the development of more effective therapeutic strategies. This research delves into the intricate dynamics of enzyme diffusion within tissue matrices, providing a comprehensive computational modeling framework aimed at optimizing enzyme concentration control in tissue scaffolds. Through advanced numerical algorithms and computational analysis, the study simulates optimal control strategies for regulating nanozyme levels, ensuring precise and sustained enzymatic activity within the scaffolds. A novel aspect of this work is the integration of videographic records, which offers an enriched understanding of the complex interactions between nanomaterials and biological tissues, providing detailed insights into the system’s operational dynamics. The study bridges the gap between experimental methodologies and theoretical models, aligning with the cutting-edge research in chemical physics and physical chemistry by offering novel approaches to tissue engineering challenges. The findings underscore the critical role of nanomaterials in achieving precise enzymatic control, which is pivotal for the development of advanced biomimetic systems and improved therapeutic outcomes. This work contributes to the ongoing frontier research in physical phenomena within chemistry, biology, and materials science, by providing significant new insights into enzyme delivery mechanisms and their application in biomedical contexts. The research not only highlights the importance of innovative methodologies in tissue engineering but also sets the stage for future experimental validations and potential clinical applications

Integrative Modeling of Signaling Network Dynamics Identifies Cell Type-Selective Therapeutic Strategies for FGFR4-Driven Cancers.

Oncogenic FGFR4 signaling represents a potential therapeutic target in various cancer types, including triple-negative breast cancer and hepatocellular carcinoma. However, resistance to FGFR4 single-agent therapy remains a major challenge, emphasizing the need for effective combinatorial treatments. Our study sought to develop a comprehensive computational model of FGFR4 signaling and to provide network-level insights into resistance mechanisms driven by signaling dynamics. An integrated approach, combining computational network modeling with experimental validation, uncovered potent AKT reactivation following FGFR4 targeting in triple-negative breast cancer cells. Analyzing the effects of cotargeting specific network nodes by systematically simulating the model predicted synergy of cotargeting FGFR4 and AKT or specific ErbB kinases, which was subsequently confirmed through experimental validation; however, cotargeting FGFR4 and PI3K was not synergistic. Protein expression data from hundreds of cancer cell lines was incorporated to adapt the model to diverse cellular contexts. This revealed that although AKT rebound was common, it was not a general phenomenon. For example, ERK reactivation occurred in certain cell types, including an FGFR4-driven hepatocellular carcinoma cell line, in which there is a synergistic effect of cotargeting FGFR4 and MEK but not AKT. In summary, this study offers key insights into drug-induced network remodeling and the role of protein expression heterogeneity in targeted therapy responses. These findings underscore the utility of computational network modeling for designing cell type-selective combination therapies and enhancing precision cancer treatment. Significance: Computational predictive modeling of signaling networks can decipher mechanisms of cancer cell resistance to targeted therapies and enable identification of more effective cancer type-specific combination treatment strategies.

Techno-Economic evaluation and multi-objective optimization of a filter equipped solar chimney system

Urban air pollution has recently become a pressing challenge, and innovative solutions are needed to reduce it. The current study proposes and investigates the performance of integrating a solar chimney system with an air filtration unit to mitigate urban air pollution and generate electricity simultaneously. A comprehensive computational fluid dynamics model coupled with optimization techniques is proposed to analyze and optimize the design parameters from system performance and cost perspectives. A multi-objective optimization is performed to determine the optimal placements of the air filter and geometric parameters of the hybrid solar chimney to maximize power output and filtration efficiency while minimizing the capital cost. Results indicated that placing the filter in the middle of the chimney resulted in optimal configuration with a power output of 18.83 kW, and filter efficiency of 53.73 % at a cost of $ 4.83 million, whereas placing the filter close to the inlet of the collector achieved the highest output power of 100.4 kW, a filter efficiency of 89.67 % at a capital cost of $ 7.35 million. It is concluded that placing the filter on the collector side is more beneficial in terms of performance and cost per unit of power produced.

Computational modelling of mouse atrio ventricular node action potential and automaticity.

The atrioventricular node (AVN) is a crucial component of the cardiac conduction system. Despite its pivotal role in regulating the transmission of electrical signals between atria and ventricles, a comprehensive understanding of the cellular electrophysiological mechanisms governing AVN function has remained elusive. This paper presents a detailed computational model of mouse AVN cell action potential (AP). Our model builds upon previous work and introduces several key refinements, including accurate representation of membrane currents and exchangers, calcium handling, cellular compartmentalization, dynamic update of intracellular ion concentrations, and calcium buffering. We recalibrated and validated the model against existing and unpublished experimental data. In control conditions, our model reproduces the AVN AP experimental features, (e.g. rate=175bpm, experimental range [121, 191]bpm). Notably, our study sheds light on the contribution of L-type calcium currents, through both Cav1.2 and Cav1.3 channels, in AVN cells. The model replicates several experimental observations, including the cessation of firing upon block of Cav1.3 or INa,r current. If block induces a reduction in beating rate of 11%. In summary, this work presents a comprehensive computational model of mouse AVN cell AP, offering a valuable tool for investigating pacemaking mechanisms and simulating the impact of ionic current blockades. By integrating calcium handling and refining formulation of ionic currents, our model advances understanding of this critical component of the cardiac conduction system, providing a platform for future developments in cardiac electrophysiology. KEY POINTS: This paper introduces a comprehensive computational model of mouse atrioventricular node (AVN) cell action potentials (APs). Our model is based on the electrophysiological data from isolated mouse AVN cells and exhibits an action potential and calcium transient that closely match the experimental records. By simulating the effects of blocking specific ionic currents, the model effectively predicts the roles of L-type Cav1.2 and Cav1.3 channels, T-type calcium channels, sodium currents (TTX-sensitive and TTX-resistant), and the funny current (If) in AVN pacemaking. The study also emphasizes the significance of other ionic currents, including IKr, Ito, IKur, in regulating AP characteristics and cycle length in AVN cells. The model faithfully reproduces the rate dependence of action potentials under pacing, opening the possibility of use in impulse propagation models. The population-of-models approach showed the robustness of this new AP model in simulating a wide spectrum of cellular pacemaking in AVN.

Model order reduction and sensitivity analysis for complex heat transfer simulations inside the human eyeball.

Heat transfer in the human eyeball, a complex organ, is significantly influenced by various pathophysiological and external parameters. Particularly, heat transfer critically affects fluid behavior within the eye and ocular drug delivery processes. Overcoming the challenges of experimental analysis, this study introduces a comprehensive three-dimensional mathematical and computational model to simulate the heat transfer in a realistic geometry. Our work includes an extensive sensitivity analysis to address uncertainties and delineate the impact of different variables on heat distribution in ocular tissues. To manage the model's complexity, we employed a very fast model reduction technique with certified sharp error bounds, ensuring computational efficiency without compromising accuracy. Our results demonstrate remarkable consistency with experimental observations and align closely with existing numerical findings in the literature. Crucially, our findings underscore the significant role of blood flow and environmental conditions, particularly in the eye's internal tissues. Clinically, this model offers a promising tool for examining the temperature-related effects of various therapeutic interventions on the eye. Such insights are invaluable for optimizing treatment strategies in ophthalmology.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

Shedding light on blue-green photosynthesis: A wavelength-dependent mathematical model of photosynthesis in Synechocystis sp. PCC 6803.

Cyanobacteria hold great potential to revolutionize conventional industries and farming practices with their light-driven chemical production. To fully exploit their photosynthetic capacity and enhance product yield, it is crucial to investigate their intricate interplay with the environment including the light intensity and spectrum. Mathematical models provide valuable insights for optimizing strategies in this pursuit. In this study, we present an ordinary differential equation-based model for the cyanobacterium Synechocystis sp. PCC 6803 to assess its performance under various light sources, including monochromatic light. Our model can reproduce a variety of physiologically measured quantities, e.g. experimentally reported partitioning of electrons through four main pathways, O2 evolution, and the rate of carbon fixation for ambient and saturated CO2. By capturing the interactions between different components of a photosynthetic system, our model helps in understanding the underlying mechanisms driving system behavior. Our model qualitatively reproduces fluorescence emitted under various light regimes, replicating Pulse-amplitude modulation (PAM) fluorometry experiments with saturating pulses. Using our model, we test four hypothesized mechanisms of cyanobacterial state transitions for ensemble of parameter sets and found no physiological benefit of a model assuming phycobilisome detachment. Moreover, we evaluate metabolic control for biotechnological production under diverse light colors and irradiances. We suggest gene targets for overexpression under different illuminations to increase the yield. By offering a comprehensive computational model of cyanobacterial photosynthesis, our work enhances the basic understanding of light-dependent cyanobacterial behavior and sets the first wavelength-dependent framework to systematically test their producing capacity for biocatalysis.

Comparative Analysis of Water Hammer Performance in Different Pipe Parameters with FSI

Water hammer (WH) is a critical phenomenon in fluid-filled piping systems that can lead to severe pressure surges and structural damage. The characteristics of the pipe material, geometry, and support conditions play a crucial role in the fluid–structure interaction (FSI) during WH events. This study investigates the impact of various pipe parameters, including material, length, thickness, and diameter, on the WH behavior using an FSI-based numerical approach. A comprehensive computational model was developed based on the algorithm presented in Delft Hydraulics Benchmark Problem (A) to simulate the WH phenomenon in pipes made of different materials, such as steel, copper, ductile iron, PPR (polypropylene random copolymer), and GRP (glass-reinforced plastic). This study examines the influence of pipe parameters on WH performance in pipelines, utilizing FSI to analyze the phenomenon. The results show that the pipe material has a significant influence on the pressure wave speed, stress wave propagation, and the overall system response during WH. Pipes with lower modulus of elasticity, such as PPR and GRP, exhibit lower pressure wave speeds but higher stress wave speeds compared with steel pipes. Increasing the elastic modulus, pipe wall thickness, length, and diameter enhances the pipe’s stiffness and impacts the timing, magnitude of pressure surges, and the likelihood of cavitation. The findings of this study provide valuable insights into the design and mitigation of WH in piping systems.

Heterogeneous Electrochemical Carbon Dioxide Reduction in Aqueous Medium Using a Novel N4-Macrocyclic Cobalt Complex.

Molecular catalysts represent an exceptional class of materials in the realm of electrochemical carbon dioxide reduction (CO2RR), offering distinct advantages owing to their adaptable structure, which enables precise control of electronic configurations and outstanding performance in CO2RR. This study introduces an innovative approach to heterogeneous electrochemical CO2RR in an aqueous environment, utilizing a newly synthesized N4-macrocyclic cobalt complex generated through a dimerization coupling reaction. By incorporating the quaterpyridine moiety, this cobalt complex exhibits the capability to catalyze CO2RR at low overpotentials and reaches near-unity CO production across a wide potential range, as verified by the online mass spectrometry and in situ attenuated total reflectance-Fourier transform infrared spectroscopy. Comprehensive computational models demonstrate the superiority of utilizing quarterpyridine moiety in mediating CO2 conversion compared to the counterpart. This work not only propels the field of electrochemical CO2RR but also underscores the promising potential of cobalt complexes featuring quaterpyridine moieties in advancing sustainable CO2 conversion technologies within aqueous environments.

(Battery Division Postdoctoral Associate Research Award Sponsored by MTI Corporation and the Jiang Family Foundation) Science of Multiphysics Behavior of Si/C Composite Anodes in High-Energy-Density Lithium-Ion Batteries

The rapidly growing demands on energy storage technologies over the last decade have imposed further requirements for the high energy/power density, safety, and durability of lithium-ion batteries (LIBs). Si/C composite materials have attracted enormous research interest as the most promising candidates for the anodes of next-generation lithium-ion batteries, owing to their high energy density and mechanical buffering property. However, the major disadvantage of materials with ultra-high capacities, such as Si-based materials, is the significant volume change during cycling, which further leads to mechanical and electrochemical degradation. However, a sophisticated and quantitative understanding of the highly electrochemical-mechanical coupling behaviors is still lacking.A comprehensive computational model is indispensable in the developing process of the excellent performance of anode material due to the low-realizability, inconvenience, and high-cost of experiments, which also provides powerful tools for fabrication guidance of novel Si/C composites designs. Hence, a multiphysics modeling framework is established with a detailed geometric description to quantitatively reveal the underlying governing mechanisms of Si/C composite anode behaviors. We studied the effects of the Si weight percentage, the Si-related particle distribution, the Si-related particle size, the mechanical constraint, and the binder domain gradient on the battery performance regarding the potential behavior, capacity delivery, mechanical stress/strain, Li plating, and polarization evolution. In our study, we used silicon monoxide (SiO) as the Si element source and graphite (Gr) as the C element source. Results discover that an 8-10 wt% of SiO would be an optimal choice regarding capacity delivery and minimizing Li plating under 1C constant current charging condition. Positioning SiO particles near the separator and reducing the sizes of SiO particles are also demonstrated to be beneficial for electrochemical performance with trivial influence on mechanical mismatch. In addition, the mechanical constraint demonstrates a balanced effect on the overall performance of cells and the local behaviors of particles. Our findings also indicate that reducing the proportion of carbon-binder domain (CBD) in the upper domain (near the anode surface) compared to the lower domain (near the current collector) positively influences electrochemical performance, particularly in terms of capacity and Li plating. However, such an arrangement introduces potential risks of mechanical failures and we recommend to incorporate a higher proportion of CBD alongside the SiO particles. Finally, an anode design with a lower CBD proportion in the upper domain exhibits superior rate performance.This study explores the multiphysics behavior of Si/C anodes material using a comprehensive methodology combining experimental and FEA techniques, systematically revealing the coupling mechanism among various physical fields, as well as providing efficient and powerful tools in the design, development, and evaluation of high energy density lithium-ion batteries.

Modeling and Experimental Validation on a Bipolar Membrane Electrodialysis Stack for Lithium Sulfate Separation

Recycling lithium-ion battery materials is a crucial step to achieving a circular economy. Among a few methods for recycling spent battery materials, a novel hydrometallurgical recycling process with leaching by acids and precipitation by bases was proposed [1]. Lithium sulfate (Li2SO4) leachate solution is generated at the end of the closed-loop recycling process. Such Li2SO4 solution can be separated by electrodialysis (ED) to form H2SO4 and LiOH solutions, which are reused for leaching and precipitation of the recycling process. The present study attempts to model this ED process and validate the model with experimental data.An ED stack based on bipolar membranes (EDBM); see Fig. 1a, was built with repetitive unit cells, each consisting of a bipolar membrane, anion exchange membrane (AEM), and cation exchange membrane (CEM). Li2SO4 enters the dilute channel and splits into SO4 2- and Li+, which travel through the AEM and CEM, respectively. Water electrolysis occurs within the bipolar membrane to produce H+ and OH-, which react with SO4 2- and Li+ to form H2SO4 and LiOH, respectively; see Fig. 1b. A two-dimensional (2D) multiphysics model is developed to elucidate the coupled transport processes within the unit cell. Conservation equations for mass, momentum, and species are solved. The transport of ions is described by the Nernst-Planck equation. Electroneutrality equations are satisfied in these membranes and channels. Electro-osmotic water transport across the membranes is considered.The effective cross-sectional area of the stack was 121.8 cm2 (70mm×174mm). The flow channel between consecutive membranes was 0.8 mm, with a mesh inserted to facilitate mixing. Experiments were carried out with the EDBM stack over different conditions such as current density, number of cell pairs, and flow rates. All experiments were operated at constant current mode [2]. The feed, acid, and base solutions were recirculated through their tanks at initial concentrations of 1.1, 0.1, and 0.1 mol/L. The ion concentrations of the solutions were measured periodically with inductively coupled plasma (ICP) mass spectrometry. The weight and the pH value of the solution tanks were also recorded.The concentration of H2SO4 is found to increase with time linearly, whereas that of LiOH levels off due to a significant electro-osmosis that drags water through the CEM into the concentrate channel. This drag reduces the performance of EDBM due to the dilution of water into the concentrate compartment. However, electro-osmotic water transport also leads to a velocity increase in the concentrate channel, which is consistent with the simulation results in our previous ED model [3]. It is found that the voltage loss across the bipolar membrane ca. 1V accounts for a significant part of the unit cell of about 1.27V.The migration of ions in electrodialysis mainly relies on diffusion and electric drive. The streamlines and the concentration distribution of Li+ under different voltages indicate that increasing voltage accelerates ion migration, see Fig. 1(c). Furthermore, the effect of the potential and concentration variations on EDBM performance is also investigated. The effects of applied current density and inlet velocity are studied on species concentrations and fluxes. Increasing the stack current density accelerates the separation process, see Fig. 1(d). It is found that increasing the solution flow rate can increase LiOH solution concentration, ion recovery rate, and current efficiency. However, the impact of the flow rate is low.This work demonstrates that EDBM can be a simple and energy-saving alternative to the current chemical precipitation method to produce LiOH from Li2SO4. The present study provides new insights into optimal operation and design for Li2SO4 EDBM. The findings are helpful in determining how the stack can be scaled up for practical application.[1] A review of recycling spent lithium-ion battery cathode materials using hydrometallurgical treatments, JCY Jung, PC Sui, J Zhang, J. Energy Storage 35, 102217, 2021.[2] Electrodialysis of a Lithium Sulphate Solution: An Experimental Investigation. B Kang, D Kang, JCY Jung, A Asadi, PC Sui, J. Electrochem. Soc. 169 (6), 063515, 2022.[3] A Comprehensive Computational Fluid Dynamics Modeling of Lithium Sulphate Electrodialysis, A Asadi, HB Harandi, JCY Jung, PC Sui, J. Electrochem. Soc. 170(9), 093502, 2023. Figure 1

Two-Dimensional Modeling of Proton Exchange Membrane Water Electrolyzer

Proton exchange membrane water electrolyzers (PEMWEs) are a central focus in the quest for clean fuel production from water. A comprehensive computational modeling of PEMWE is essential for predicting and optimizing the performance of PEMWE, enabling efficient design and advancement in clean energy technologies. In this study, a 2-D, transient, multiphysics, and multiphase model is developed to study the performance of PEMWE with a focus on morphological and geometrical features of porous transport layer (PTL), liquid saturation, gas pressure, and operating temperature. User Defined Function (UDF), an add-on of Ansys Fluent® is used to solve the transport equations in different layers of the PEMWE model. The model is first validated against experimental data (figure 1) from the literature. Mass transfer of each species (hydrogen, oxygen, vapor, and dissolved water) and heat transfer are analyzed in each layer of PEMWE, including PTL, anode catalyst layer, membrane, cathode catalyst layer, and gas diffusion layer. Besides, the electronic and ionic potential in all domains, along with the liquid water saturation distribution in porous layers, are investigated. Different governing parameters such as cell voltage, and operating temperature are varied to get the performance of the electrolyzer cell. The findings will provide new insights into electrolyzer cell performance under various operating conditions, offering valuable information for adjusting parameters and optimizing the design of electrolyzer cells. Figure 1

Plant Circadian Clocks: Unravelling the Molecular Rhythms of Nature

The circadian clock is a fundamental biological mechanism that allows organisms to synchronize their internal processes with the external environment, thereby optimizing growth, development, and physiology. In plants, circadian rhythms govern various aspects of their life cycle, including germination, leaf movement, flowering, and responses to environmental cues such as light and temperature. Understanding the molecular mechanisms underlying plant circadian clocks is essential not only for elucidating fundamental principles of plant biology but also for applications in agriculture and biotechnology. This review delves into the intricate molecular networks that comprise plant circadian clocks, focusing on key components and their interactions. At the core of these clocks are transcription-translation feedback loops (TTFLs) involving a set of clock genes. The central oscillator, composed of genes such as CIRCADIAN CLOCK ASSOCIATED 1 (CCA1), LATE ELONGATED HYPOCOTYL (LHY), TIMING OF CAB EXPRESSION 1 (TOC1), and PSEUDO-RESPONSE REGULATOR (PRR) genes, drives rhythmic expression of downstream clock-controlled genes (CCGs). The interplay between positive and negative regulators within the TTFLs generates robust oscillations with a period of approximately 24 hours. Additionally, post-translational modifications, protein-protein interactions, and chromatin remodeling contribute to fine-tuning the circadian system, allowing plants to adapt to changing environmental conditions. Light perception through photoreceptors such as phytochromes and cryptochromes plays a crucial role in entraining the circadian clock to external cues, ensuring synchronization with the day-night cycle. Furthermore, recent advancements in omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have provided unprecedented insights into the complexity and plasticity of plant circadian clocks. Integration of multi-omics data has facilitated the construction of comprehensive regulatory networks and computational models, enabling predictive understanding of clock function and behavior under diverse conditions. Overall, unraveling the molecular rhythms of plant circadian clocks not only enhances our knowledge of fundamental biological processes but also holds promise for improving crop productivity, stress resilience, and sustainability in agriculture through targeted manipulation of clock components and their associated pathways. Future research endeavors will undoubtedly continue to unveil the intricacies of these fascinating timekeeping mechanisms, further enriching our understanding of the molecular rhythms of nature.

Performance of the bond between recycled concrete and steel bars subject to transverse stirrup constraints after exposure to high temperatures

This study investigates and assesses the bond performance between steel reinforcement and recycled concrete, subject to the confining effect of transverse stirrups post high-temperature exposure. A series of 24 specimen groups was examined, with variations in parameters including transverse stirrup ratios (0 %, 0.24 %, 0.67 %, 1.34 %), recycled concrete aggregate (RCA) replacement ratios (0 %, 50 %, 100 %), and temperatures (300 °C, 500 °C, 700 °C). Central pull tests were administered to discern the impact of each parameter on the bond integrity of steel-reinforced recycled concrete. The results revealed that high temperatures reduced the thickness of the critical cover, altering the failure mode of the specimen. With rising temperatures, the bond strength of steel-reinforced recycled concrete weakened. Nonetheless, an increase in stirrup ratio served to counteract the detrimental effects of high temperatures. In contrast, a higher RCA replacement ratio exacerbated these effects. For example, specimens with a 1.34 % stirrup ratio showed a 30 % decrease in the adverse impacts of high temperature on bond strength compared to those with no stirrups. A comprehensive computational model, devised from experimental data and theoretical analysis, predicts the bond-slip behavior of steel-reinforced recycled concrete, incorporating the constraint coefficient k, temperature T, and replacement ratio r as pivotal parameters. The model’s predictive curve corresponds closely with the experimental findings, effectively capturing the influence of high temperature, lateral restraint, and RCA replacement ratio on the bond performance of steel-reinforced recycled concrete.

A comprehensive MRI-based computational model of blood flow in compliant aorta using radial basis function interpolation

BackgroundProperly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall.MethodsWe performed two different types of simulations—static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol.ResultsThe simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain).ConclusionsIn conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.

Influence of pneumatic tire enveloping behavior characteristics on the performance of a half car suspension system using multi-objective optimization algorithms

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design.

Modeling and self-supporting printing simulation of fuse filament fabrication

This study presented a comprehensive computational fluid dynamics-based model for fused filament fabrication (FFF) three-dimensional (3D) printing multiphase and multiphysics coupling. A model based on the framework of computational fluid dynamics was built, utilizing the front-tracking method for high precision of multiphase material interfaces, a fully resolved simulation at the mesoscale explores the underlying physical mechanism of the self-supported horizontal printing. The study investigated the influence of printing temperature and velocity on the FFF process, exhibiting a certain self-supporting forming ability over a specific range. The results indicated that during the printing of large-span horizontal extension structures, the bridge deck material transitions from initial straight extension to sagging deformation, ultimately adopting a curved shape. The straight extension distance is inversely proportional to the depth of the sagging deformation. Additionally, the study revealed that printing temperature primarily affected the curing time of the molten material, while printing velocity fundamentally affected the relaxation time of both thermal and dynamic characteristics of the material.

Activation energy and angular momentum in electromagnetic radiation micropolar nanofluids: Focus on microbial eukaryotic species

Microbial eukaryotic species play a crucial role in biomedical research, aiding in the study of bacterial impurities, microorganism pathogenesis, and development of tactics to combat antimicrobial struggle. These species represent a complex and diverse aspect of bacteriological life, influencing ecosystems, human health, and technological progress. This study aims to create a comprehensive computational model to analyze the magnetohydrodynamics of a non-Newtonian micropolar nanofluid over an exponentially stretchable surface. It incorporates the Buongiorno model, which considers thermophoretic diffusion and Brownian motion effects to study the thermal behavior of micropolar nanofluids. It is also investigated how thermal radiation affects the current model. The classical Navier's stokes equations of motions of the current model are transform into a system of ordinary differential equation by employing similarity approach. The classical Navier-Stokes equations of motion are transformed into ordinary differential equations using similarity transformations, and MATLAB is used to solve them numerically. Here we considered three different cases, (i)-injection fw>0, (ii)-impermeable wall fw=0, (iii)-suction fw<0 which are illustrated visually. The consequence of various flow parameters effects on velocity, temperature, concentration, motile microorganism, and angular profiles are illustrated graphically. The high concordance between the model's results and the available data validates its originality. Present results indicate that while a larger porosity and Hartman number decrease velocity, increasing the mass and thermal convective factors raises the velocity profile. Raising the thermophoretic and magnetic parameters causes the temperature profile to climb; however, raising the temperature exponent and Prandtl number causes it to fall. The behavior of micropolar nanofluids is better understood thanks to this research, which could have applications in a variety of industries including manufacturing, energy, electronics, and healthcare. The novelty of the current model is verified using data that has already been published, and a great agreement is observed. When the values of the parameter K lies between the ranges 0.2<K< 0.3, and 0.3<K< 0.4, percentage increment noted in heat transfer enhancement is about 2.3 % and 6.3 % Until now, no such investigation has been performed to examine the implications of three-dimensional bio-convection micropolar based Casson fluid flow under the activation energy and suction/injection. From the outcomes, it was established that nanofluids are more fruitful for heat transfer enhancement.

Numerical simulation of cone penetration test by using CFD–DEM coupled analysis

Precise stratigraphic characterization and assessment of soil parameters are essential for agricultural and geotechnical engineering. The cone penetration test (CPT) has become one of the most extensively used techniques for soil site assessment, because of its reproducibility, robustness, accuracy, and simplicity. The existing DEM (discrete element method) simulations on CPT are only applicable to dry soil, which cannot consider fluid phase (i.e., pore water) and its interaction with the soil particles. The combined DEM and CFD (computational fluid dynamics) approach is developed to model CPT testing on saturated soils in this study. Several sets of CPT simulations at various penetration rates have been performed by using CFD–DEM coupled analysis. The variation of penetration velocity leads to different magnitudes of fluid force, and the variation in fluid force, in turn, affects the CPT measurement of soil’s characteristics. Furthermore, the study extends beyond the properties of the soil itself to explore the complex interplay among soil particles, the surrounding fluid environment, and the penetrometer. The cumulative interactions among these elements highlight the intricate nature of CPT and underline the importance of comprehensive computational models in enhancing our understanding of these dynamics.