Computational fluid dynamics
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Pluronics, also known as poloxamers, are amphiphilic triblock copolymers widely employed in drug delivery systems due to their tunable self-assembly and biocompatibility. Among them, Pluronic F68 (Poloxamer 188) exhibits thermoresponsive behavior in aqueous solution, forming ordered supramolecular structures at high concentrations and temperatures. In this work, we investigate the morphological and rheological properties of a 45 wt% Pluronic F68 aqueous system at different temperatures through a combination of experimental and computational approaches. Rheological measurements and Small-Angle X-ray Scattering (SAXS) confirm the formation of a body-centered cubic (BCC) structure at higher temperatures and highlight the emergence of viscoelastic solid-like behavior. To support and extend these findings, Dissipative Particle Dynamics (DPD) simulations are employed to model the nanostructure evolution and the impact of temperature on self-assembly and material properties. This integrated approach provides a consistent framework to characterize the temperature-induced transition from fluid-like to solid-like states and sets the groundwork for future simulation studies incorporating drug cargo. The results offer valuable insights into the design of thermoresponsive drug delivery systems and demonstrate the potential of DPD in capturing complex structure-property relationships in amphiphilic polymer systems.
- Klíčová slova
- Dissipative particle dynamics, Drug delivery systems, Pluronics, Rheology, Self-assembly,
- MeSH
- maloúhlový rozptyl MeSH
- nosiče léků * chemie MeSH
- poloxamer * chemie MeSH
- povrchové vlastnosti MeSH
- reologie MeSH
- teplota MeSH
- velikost částic MeSH
- voda * chemie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- nosiče léků * MeSH
- poloxamer * MeSH
- voda * MeSH
INTRODUCTION: Intraventricular hematoma (IVH) occurs in approximately 40% of acute intracerebral hemorrhage (ICH) patients and is significantly associated with worse clinical outcomes. According to cerebrospinal fluid dynamics, some blood within the ventricles may circulate through the subarachnoid spaces, leading to its apparent "disappearance" on follow-up imaging. We aim to investigate the association between initial IVH involvement and significant early ICH retraction at follow-up imaging. METHODS: Data are from the MCAHP (Multiphase CT Angiography Hematoma Prediction) Study, which included consecutive patients with acute ICH investigated with multimodal CT imaging. Patients who underwent surgery before follow-up imaging were excluded. IVH severity was assessed using the IVH score. The primary outcome was significant early ICH retraction, defined as volume decrease (⩾3 ml or ⩾15%) between the initial and follow-up scans. Secondary outcomes included early absolute and relative ICH decrease. Associations between outcomes and initial IVH involvement or IVH score were assessed with logistic regression adjusted for age, baseline NIHSS, initial ICH volume, and onset-to-CT time. RESULTS: Overall, 177 ICH patients were included. The median age was 71 years (IQR = 59-80), 71 (40.1%) patients were female, and 64 (36.2%) presented with initial IVH involvement. Patients with initial IVH, compared to those without, had a larger initial ICH volume (28.5 ml [IQR = 12.7-52.5] vs. 18.9 ml [IQR = 8.1-30.6], p < 0.001) and different ICH location (deep = 54.7% vs 47.8%; lobar = 35.9% vs 46.0%; infratentorial = 7.3% vs 6.2%; p < 0.001). Early ICH retraction was observed in 33 (18.6%) patients: 21 (32.8%) with initial IVH and 10 (10.6%) without initial IVH. There was a significant association between early ICH retraction and initial IVH involvement (adjusted odds ratio [aOR] 4.02 [95% CI = 1.72-9.41]) and IVH score (aOR 1.14 [95% CI = 1.05-1.23] per 1-point increase). Similar results were observed for secondary outcomes. CONCLUSION: Initial IVH involvement is associated with early ICH retraction - "intraventricular washout." This might result in an underestimation of hematoma expansion occurrence and severity in these patients, with potential implications when evaluating the predictive performance of hematoma expansion markers/scores and the radiological efficacy of hemostatic treatments.
- Klíčová slova
- Hemorrhagic stroke, cerebrospinal fluid, hematoma expansion, intracerebral hemorrhage, intracranial hemorrhage,
- MeSH
- cerebrální krvácení * diagnostické zobrazování MeSH
- CT angiografie MeSH
- hematom * diagnostické zobrazování MeSH
- intraventrikulární krvácení do mozku * diagnostické zobrazování MeSH
- lidé středního věku MeSH
- lidé MeSH
- mozkové komory * diagnostické zobrazování MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Dissipative particle dynamics (DPD) is an incredibly powerful tool for simulating the behavior of structured fluids. However, identifying the appropriate model parameters to accurately replicate physical properties remains a challenge. This study showcases the benefits of integrating machine learning techniques into the top-down parameterization of Pluronic systems. The proposed workflow outlines a data-driven approach to accurately determine model parameters tailored to various Pluronic systems. Gaussian process regression (GPR)-based surrogate models effectively replicate the results of DPD simulations, delivering faster responses that streamline parameter optimization and enable the calibration of Pluronic systems against experimental data. Although DPD simulations provide valuable insight, their high computational cost, due to extensive simulations and post-processing, presents a challenge. The GPR-based surrogate model addresses this by modeling the relationships between input parameters and output properties. SHAP (SHapley additive exPlanations) analysis enhances model interpretability, providing deeper insights into the relationships and causal mechanisms between the input parameters and the predicted properties. The combination of GPR and SHAP analysis provides an interpretable machine learning approach, enabling a more efficient optimization process and reducing the need for exhaustive simulations. This work lays a foundation for generalizing the parameterization process across Pluronic systems and conditions, such as varying temperatures, by incorporating additional DPD model input parameters.
- Publikační typ
- časopisecké články MeSH
Industrial combustion systems are among the primary contributors to nitrogen oxide (NOx) emissions, posing challenges for air quality management and regulatory compliance. This study presents a computational and data-driven approach to the design and optimization of a natural gas burner employing a folded flame pattern with fuel staging. Using Computational Fluid Dynamics (CFD) simulations combined with Machine Learning (ML)-assisted predictive modeling, the burner geometry, fuel-air mixing behavior, and heat transfer dynamics were systematically optimized. A Support Vector Regression-based model was trained on CFD-generated data to guide design modifications and reduce reliance on trial-and-error experimentation. The resulting burner design achieved a 31% reduction in NOx emissions while maintaining combustion efficiency and improving flame stability. Lower peak flame temperatures contributed to reduced pollutant formation. Particle tracing analysis revealed recirculation zones that promoted optimal fuel-air mixing and heat transfer. This integrated CFD-ML framework demonstrates a scalable solution for cleaner combustion design. Future work will focus on experimental validation and the adaptability of the burner to alternative fuels such as hydrogen-rich blends and biogas, aiming to extend the applicability of this approach across diverse industrial settings.
- Klíčová slova
- CFD optimization, Combustion efficiency, Emission control, Fuel staging, Machine learning, NOx reduction,
- Publikační typ
- časopisecké články MeSH
PURPOSE: Pituitary adenoma, a relatively common intracranial tumor, is often treated surgically through the nasal cavity, which alters its anatomy. This study aims to determine the severity of these changes in airflow and flow distribution within the nasal cavity, focusing on the anterior nasal region's role in airflow redistribution. Computational fluid dynamics (CFD) was employed to analyze these changes before and after surgery. METHODS: Data from four patients of the Department of Neurosurgery and Neuro-oncology of the Military University Hospital, Prague, were analyzed using CFD simulations in Ansys Fluent 2021 R1. Computed tomography (CT) scans were used to model the nasal cavities pre- and post-surgery, creating polyhedral meshes of 1.8 million cells before surgery and 2.2 million cells after surgery. The k-ε turbulent model was applied to compute flow fields, providing consistent results across patients. RESULTS: The surgery increased the nasal cavity volume, primarily due to the endonasal transsphenoidal approach. Cross-sectional areas, particularly in the middle nasal meatus, were enlarged, reducing airflow velocity without altering total volume flow. Most airflow was redistributed through the middle nasal meatus, while flow in peripheral regions decreased. The anterior part of the nasal cavity was identified as having the most significant influence on airflow redistribution. CONCLUSION: Surgery impacts nasal anatomy and airflow dynamics significantly, particularly in the anterior part of the nasal cavity. These findings emphasize the need for surgical precision to minimize unintended shifts in airflow patterns. Further studies are recommended to validate these observations.
- Klíčová slova
- adenomas, computational fluid dynamics (cfd), computed tomography (ct), nasal cavity, pituitary tumour,
- Publikační typ
- časopisecké články MeSH
This study investigates the influence of varying degrees of stenosis on blood flow within elliptic arteries, emphasizing the critical role of artery shape in clinical evaluations as opposed to the commonly studied circular arteries. Unlike prior work, this research offers a precise definition of stenosis by incorporating the measured length, height, and position of the narrowing. Employing the non-Newtonian Williamson fluid model, we conducted comprehensive numerical simulations to examine blood flow through four distinct stenosis formations. The novelty of this work lies in its accurate modeling of stenosis and use of advanced mesh generation, combined with commercial software and the finite volume method, to capture detailed hemodynamic behavior. Visualized results, including pressure profiles, velocity line graphs, and streamlines, further underscore the distinctive flow dynamics shaped by the elliptic geometry. Key findings of the obtained results reveal that blood velocity peaks near the stenosis and drops significantly post-stenosis, with notable variations in flow patterns, energy loss, and pressure distribution across different stenosis types. Further, higher velocity of blood flow is observed in elliptic arteries in comparison with circular ones. In the area of the high corners of stenotic segments, the pressure profile reaches high values. As a result of the narrowing of the arterial cross-section, the varied time shows that the post-stenotic segment of the artery has a higher pressure than the pre-stenotic section. The varied time suggests that an axially symmetric profile will eventually be the norm for the flow within the arterial portion. These insights have profound implications for improving clinical diagnosis and treatment strategies for conditions related to stenosed elliptic arteries.
Continuous flow reactors are promising for electrochemical conversions, in large part due to the potentially rapid refreshment of reagents over the electrode surface. Microfluidic reactors enable a high degree of control over the fluid flow. Diffusion to and from the electrode and electrode area determine the efficiency of electrochemical conversion. The effective electrode area is limited by the loss in electrode potential due to iR drop, and further electrode length (and hence area) is limited due to ineffective mass transport to and from the electrode. Here, we report on a microfluidic electrochemical device with large (long) area electrodes running in parallel, which both minimizes the iR drop and ensures a constant electrode potential along the whole length of the electrodes. The electrodes are separated by laminar flow in the channels, instead of by a membrane, thereby reducing cell resistance. Herringbone grooves are used to increase mass transport rates by inducing transverse flow. We confirm fluid flow behavior in the devices using computational fluid dynamics (CFD) and verify the results experimentally using in-line and off-line UV/vis absorption and resonance Raman spectroscopy. We anticipate that this approach will aid future development of electrochemical flow reactors, enabling larger area-electrodes and realizing greater efficiencies.
- Publikační typ
- časopisecké články MeSH
This study presents the experimental and theoretical modeling results of pressure changes caused by fluid flow in a water aspirator (water pump) whose working principle is based on the Venturi effect. Experimentally measured pressure drop in a glass-made device is modeled in COMSOL Multiphysics by previously reproducing the device geometry. Computations were performed using a Fluid Flow Module with turbulence model RANS k-ε. Pressure and liquid velocity magnitude maps were drowned, and selected vertical and perpendicular cross-sections of velocity and pressure fields were shown and discussed, indicating model limitations.
- Klíčová slova
- COMSOL, Finite element method, Turbulent flow, Venturi effect, Water pump,
- Publikační typ
- časopisecké články MeSH
There is, at present, a lack of consensus regarding precisely what is meant by the term 'energy' across the sub-disciplines of neuroscience. Definitions range from deficits in the rate of glucose metabolism in consciousness research to regional changes in neuronal activity in cognitive neuroscience. In computational neuroscience virtually all models define the energy of neuronal regions as a quantity that is in a continual process of dissipation to its surroundings. This, however, is at odds with the definition of energy used across all sub-disciplines of physics: a quantity that does not change as a dynamical system evolves in time. Here, we bridge this gap between the dissipative models used in computational neuroscience and the energy-conserving models of physics using a mathematical technique first proposed in the context of fluid dynamics. We go on to derive an expression for the energy of the linear time-invariant (LTI) state space equation. We then use resting-state fMRI data obtained from the human connectome project to show that LTI energy is associated with glucose uptake metabolism. Our hope is that this work paves the way for an increased understanding of energy in the brain, from both a theoretical as well as an experimental perspective.
- Klíčová slova
- Computational neuroscience, Neural energy,
- Publikační typ
- časopisecké články MeSH
Stratified flows are commonly observed in numerous industrial processes. For example, a gas-condensate pipeline typically uses a stratified flow regime. However, this flow arrangement is stable only under a specific set of operating conditions that allows the formation of stratification. In this study, the authors analyzed the flow attributes of Prandtl Eyring liquid past an inclined sheet immersed in a stratified medium. The flow also characterizes the features of the magnetic field along with a first-order chemical reaction. Convective boundary constraints associated with the thermosolutal exchange at the extremity of the domain are also prescribed. The fundamental equations of the study are formulated in dimensional PDEs and converted into dimensionless ODEs via similar variables. The numerical solution of the modelled setup is acquired by executing computations using shooting and RK-4 methods. The intelligent computing paradigm working on the mechanism of the back-propagated Levenberg-Marquardt strategy is also capitalized to forecast the behavior of related physical quantities. Graphs and tables are drawn to elaborate the impression of pertinent factors on flow distributions. It is perceived that the momentum profile diminishes with the magnetic field effect, whereas the opposite behavior is observed for the skin friction coefficient. The thermal and concentration distributions were found to dominate in the absence of stratification. Consideration of convective heating and concentration tends to elevate thermal and mass distributions.
- Klíčová slova
- Artificial neural networking, Chemical reaction, Convective boundary constraints, MHD, Prandtl-Eyring fluid, Thermosolutal stratification,
- Publikační typ
- časopisecké články MeSH
