Magnetic hyperthermia is a promising cancer treatment method that involves complex multiphysics phenomena, including interstitial tissue fluid flow, magnetic nanoparticle (MNP) transport, and temperature evolution. However, these intricate processes have rarely been studied simultaneously, primarily due to the lack of a comprehensive simulation tool. To address this issue, we develop a comprehensive numerical framework in this study. Using this framework, we simulate a circular-shaped tumor embedded in healthy tissue. The treatment process is examined under two scenarios: one considering gravity and the other neglecting it. Without gravity, the interstitial tissue flow remains stationary, and hence MNP transport and temperature evolution are determined solely by diffusion. The optimal treatment time, when the tumor cells are completely ablated, decreases with both the Lewis number and the heat source number, following a power law. When gravity is considered, treatment efficacy deteriorates due to buoyancy-induced MNP movement, significantly extending the time required to completely ablate the tumor cells. This required time increases with both the buoyancy ratio and the Darcy ratio, also following a power law. The results from this study could provide valuable guidelines for practical magnetic hyperthermia treatment.

We report on numerical studies of bounded double-diffusive turbulent convection, which involves the combined effects of concentration/solutal and thermal buoyancy forces. Our study focuses on an intermediate range of the characteristic non-dimensional numbers, specifically 107≤Rac≤109, and 0≤Raθ≤106. We use fixed values for the concentration and temperature Prandtl numbers (i.e. Prc=700, Prθ = 7), which approximately correspond to seawater properties. We apply wall-resolved Large Eddy Simulations (LES) and compare the obtained results with available Direct Numerical Simulations (DNS) in the literature. Our findings show an overall good agreement in predicting the global wall mass and heat transfer coefficients, achieved with significantly reduced computational costs. Furthermore, the local mass and heat transfer distributions reveal a high sensitivity to the strength of the vertically imposed stable thermal stratification. Finally, we present the vertical profiles of the long-term time-averaged first and second moments.

Targeted drug delivery to the deep lung improves therapeutic outcomes, but respiratory system variability complicates drug spray design. Numerical simulations offer insights for individualized treatments but are computationally intensive, highlighting the need for surrogate models for real-time deposition prediction. This study comprehensively explores the multi-task predictive capability of regression models, including Linear regression (LR), Bayesian regression (BR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and CatBoost, for predicting total and regional deposition rates of inhaled particles in airway. A training dataset is obtained from well-validated CFD simulations with realistic human airway model using Euler-Lagrangian method. The results indicate that LR, BR, and SVM yield unsatisfactory predictive accuracy, with average R² values in range of 0.21 to 0.73. Comparatively, BPNN and decisiontree-based models show great potential in predicting total deposition rate in the upper and central airway. However, for regional deposition rate prediction, BPNN did not consistently yield high accuracy, particularly for oral deposition (R² = 0.538). Comparatively, XGBoost emerges as optimal model, achieving an R² approximately close to 1 on both the training and testing datasets, with predictive errors within the range of ±0.5. The overall results demonstrate that decision-tree-based models, particularly XGBoost, have superior performance in accurately predicting both total and regional deposition rates of inhaled particles within airway. Despite limitations like geometry complexity and data quantity, the workflow developed in this study is expected to pave the way for future research integrating ML models into drug delivery device design and evaluation.

Several studies have recommended the use of hydrogels for localized targeted delivery of chemotherapeutic drugs following tumor removal surgery. This approach aims to both fill the cavity and prevent cancer recurrence. The use of Multiphysics-based simulation emerges as a valuable strategy for minimizing experimental work, providing detailed insights into how drug release occurs in the tissue, and enabling the optimization of the design. In this study, we introduced a mathematical model, utilizing experimental data, to investigate the transport of liposomes carrying MZ1 from a thermosensitive hydrogel and their impact on the viability of breast cancer cells. The proposed comprehensive model considers not just the transport within the interstitial tissue, represented as a porous medium, but also the uptake by cells and its influence on cell viability, along with the potential lymphatic drainage. The six real patient-specific tumor shapes extracted from MRI scans were used to investigate how the size and form of the tumor can modify the transport pattern. The computational results revealed that the concentration of liposomes in the tissue is significantly influenced by their release from the hydrogel, which proved to be the limiting step. Liposome concentrations of approximately 0.1 % weight were found to be sufficient in ensuring minimal cell survival in the vicinity of the tumor.

In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40 subject-specific aortic geometries obtained from 4D Flow MRI, we applied statistical shape modeling to generate 1,000 synthetic aorta geometries. Complete computational fluid dynamics (CFD) simulations of these geometries were performed to obtain ground-truth values. We then trained deep neural networks for each characteristic flow feature using 900 randomly selected aorta geometries. Testing on remaining 100 geometries resulted in average errors of 3.11% for velocity and 4.48% for pressure. For wall shear stress predictions, we applied two approaches: (i) directly derived from the neural network-predicted velocity, and, (ii) predicted from a separate neural network. Both approaches yielded similar accuracy, with average error of 4.8 and 4.7% compared to complete 3D CFD results, respectively. We recommend the second approach for potential clinical use due to its significantly simplified workflow. In conclusion, this proof-of-concept analysis demonstrates the numerical robustness, rapid calculation speed (less than seconds), and good accuracy of the CFD-based machine learning approach in predicting velocity, pressure, and wall shear stress distributions in subject-specific aortic flows.

<Note to JM: Errors in article heads, particularly titles, author names, affiliations must not be repeated. In such cases, use the correct title, authors, affiliation(s), etc. If the corrigendum is the result of authorship changes, include the correct author details in the article head.>

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Thoracic aortic aneurysm (TAA) is a local dilation of the thoracic aorta. Although universally used, aneurysm diameter alone is a poor predictor of major complications such as rupture. There is a need for better biomarkers for risk assessment that also reflect the aberrant flow patterns found in TAAs. Furthermore, hypertension is often present in TAA patients and may play a role in progression of aneurysm. The exact relation between TAAs and hypertension is poorly understood. This study aims to create a numerical model of hypertension in the aorta by using computational fluid dynamics. First, a normotensive state was simulated in which flow and resistance were kept unaltered. Second, a hypertensive state was modeled in which blood inflow was increased by 30%. Third, a hypertensive state was modeled in which the proximal and peripheral resistances and capacitance parameters from the three-element Windkessel boundary condition were adjusted to mimic an increase in resistance of the rest of the cardiovascular system. One patient with degenerative TAA and one healthy control were successfully simulated at hypertensive states and were extensively analyzed. Furthermore, three additional TAA patients and controls were simulated to validate our method. Hemodynamic variables such as wall shear stress, oscillatory shear index, endothelial cell activation potential (ECAP), vorticity and helicity were studied to gain more insight on the effects of hypertension on flow patterns in TAAs. By comparing a TAA patient and a control at normotensive state at peak-systole, helicity and vorticity were found to be lower in the TAA patient throughout the entire domain. No major changes in flow and flow derived quantities were observed for the TAA patient and control when resistance was increased. When flow rate was increased, regions with high ECAP values were found to reduce in TAA patients in the aneurysm region which could reduce the risk of thrombogenesis. Thus, it may be important to assess cardiac output in patients with TAA.

Numerous studies in the literature have proposed the use of thermo-responsive hydrogels for filling cavities after tumor resection. However, optimizing the injection process is challenging due to the complex interplay of various multi-physics phenomena, such as the coupling of flow and heat transfer, multi-phase interactions, and phase-change dynamics. Therefore, gaining a fundamental understanding of these processes is crucial. In this study, we introduce a thermo-sensitive hydrogel formulated with poloxamer 407 and Gellan gum as a promising filling agent, offering an ideal phase-transition temperature along with suitable elastic and viscous modulus properties.

We performed multi-physics simulations to predict the flow and temperature distributions during hydrogel injection. The results suggested that the hydrogel should be kept at 4 °C and injected within 90 s to avoid reaching the transition temperature. Cavity filling simulations indicated a symmetric distribution of the hydrogel, with minimal influence from the syringe's position.

The temperature gradient at the cavity edge delays gelation during injection, which is essential to guarantee its administration as a liquid. The hydrogel's viscosity follows a sigmoidal function relative to temperature, taking five minutes to reach its maximum value. In summary, the multi-physics simulations carried out in this study confirm the potential of thermo-responsive hydrogels for use in post-tumor surgery treatment and define the conditions for a proper administration. Furthermore, the proposed model can be widely applied to other thermo-responsive hydrogels or under different conditions.

This paper presents a study of developing a numerical turbulent model in a cavity heated from below using eddy viscosity combined with elliptic relaxation approach. The model uses a set of differential equations that consist of kinetic energy, its dissipation, variance of temperature, velocity scale and elliptic relaxation parameter, which are solved using a finite-volume and Navier-Stokes solver. The unresolved stress tensors and heat flux vectors are modelled with an algebraic formula. The discretization method is carried out by CDS, or second-order differencing scheme, and LUDS, or second-order linear upwind scheme. The model is applied to the natural convection heated from below, known as Rayleigh-Benard convection, in a two-dimensional cavity with a height-to-length aspect ratio of 1:1.5, 1:4, and 1:8. The model has been validated using numerical data from DNS (direct numerical simulation) and experiments. The model produced similar results with both DNS and experiments. It was also shown that the model can visualize the main feature of turbulent convective flow in the enclosure for various aspect ratio.

The treatment for asthma and chronic obstructive pulmonary disease relies on forced inhalation of drug particles. Their distribution is essential for maximizing the outcomes. Patient-specific computational fluid dynamics (CFD) simulations can be used to optimize these therapies. In this regard, this study focuses on creating a parametric model of the human respiratory tract from which synthetic anatomies for particle deposition analysis through CFD simulation could be derived. A baseline geometry up to the fourth generation of bronchioles was extracted from a CT dataset. Radial basis function (RBF) mesh morphing acting on a dedicated tree structure was used to modify this baseline mesh, extracting 1000 synthetic anatomies. A total of 26 geometrical parameters affecting branch lengths, angles, and diameters were controlled. Morphed models underwent CFD simulations to analyze airflow and particle dynamics. Mesh morphing was crucial in generating high-quality computational grids, with 96% of the synthetic database being immediately suitable for accurate CFD simulations. Variations in wall shear stress, particle accretion rate, and turbulent kinetic energy across different anatomies highlighted the impact of the anatomical shape on drug delivery and deposition. The study successfully demonstrates the potential of tree-structure-based RBF mesh morphing in generating parametric airways for drug delivery studies.

The paper reports on the numerical turbulence model in predicting mass, momentum and heat transfer in a partially divided cavity heated from the side using buoyancy-extended eddy-viscosity and elliptic relaxation approach with the algebraic expressions for the Reynold stress tensor and turbulent heat flux vector. The CDS (central differencing scheme) and LUDS (linear upwind differencing scheme) were used as the discretization method and the governing equations were solved using the finite volume method and Navier-Stokes solver. Validation of the model has been carried out by experimental data of convective flow in the cavity as well as by numerical data DNS (direct numerical simulation). The model agrees very well with the experiment and DNS and it is also able to demonstrate the performance which is comparable to that of the previous advanced second-moment closure model (SMC) in the literature. The results show that the model is suitable for use in simulations of the turbulent convective flow in a cavity with partition and it has the potential to be applied to more complex cavities and a wide range of turbulence levels.

BACKGROUND: Kidney disease is the most important predictor of death in patients with a Fontan circulation, yet its clinical and hemodynamic correlates have not been well established. METHODS AND RESULTS: A total of 53 ambulatory patients with a Fontan circulation (median age, 16.2 years, 52.8% male patients) underwent advanced cardiovascular magnetic resonance assessment, including 4-dimensional flow imaging and computational fluid dynamics. Estimated glomerular filtration rate (eGFR) <90 mL/min per 1.73 m2 was observed in 20.8% and albumin-to-creatinine ratio >3 mg/mmol in 39.6%. The average eGFR decline rate was -1.83 mL/min per 1.73 m2 per year (95% CI, -2.67 to -0.99; P<0.001). Lower eGFR was associated with older age, larger body surface area at examination, longer time since Fontan procedure, and lower systemic ventricular ejection fraction. Higher albumin-to-creatinine ratio was associated with absence of fenestration at the Fontan operation, and older age and lower systemic ventricular ejection fraction at the assessment. Lower cross-sectional area of the Fontan conduit indexed to flow (r=0.32, P=0.038), higher inferior vena cava-conduit velocity mismatch factor (r=-0.35, P=0.022), higher kinetic energy indexed to flow in the total cavopulmonary connection (r=-0.59, P=0.005), and higher total cavopulmonary connection resistance (r=-0.42, P=0.005 at rest; r=-0.43, P=0.004 during exercise) were all associated with lower eGFR but not with albuminuria. CONCLUSIONS: Kidney dysfunction and albuminuria are common among clinically well adolescents and young adults with a Fontan circulation. Advanced cardiovascular magnetic resonance-derived metrics indicative of declining Fontan hemodynamics are associated with eGFR and might serve as targets to improve kidney health. Albuminuria might be driven by other factors that need further investigation.

The turbulent penetrative convection into a stable convective boundary layer represents an important phenomenon in environmental engineering and atmospheric science. In the present study, we present a series of numerical simulations performed by two modeling approaches: the high-fidelity Large-Eddy Simulations (LES), and the less computationally demanding transient Reynolds-Averaged Approach (TRANS), but with an advanced sub-scale turbulent heat flux model. By simulating different localized heat sources over the ground, and by performing a direct comparative assessment of results obtained by LES and TRANS, we confirmed an overall good agreement in predicting the time evolution of the horizontally averaged temperature profiles. Similarly, the morphology of instantaneous thermal plumes and large convective structures predicted by TRANS were in reasonable agreement with the referent LES predictions.

In the present study, we have applied a combined wall-resolving dynamic Large-Eddy Simulation (LES) (for the velocity field) and Direct Numerical Simulation (DNS) (for the temperature field) approach for mixing of parallel triple-jets with different temperatures of liquid sodium in a turbulent forced convection regime. Because of the high thermal conductivity of sodium (a low-Prandtl fluid), we adopted the dynamic Smagorinsky subgrid closure for the unresolved velocity scales, while the thermal scales are fully resolved. Furthermore, the Time-dependent Reynolds-Averaged Navier-Stokes (T-RANS) approach with the high-Reynolds number variant (i.e. with the wall functions as boundary conditions along solid boundaries) of the four-equation eddy viscosity model (k−ε−k_θ−ε_θ) was applied. The fine-mesh LES/DNS provided a close agreement with the experimental data for both velocity and temperature fields (for both first- and second-moments). In contrast, the coarse-mesh LES/DNS overestimated the turbulent kinetic energy profiles at different distances from the inlet plane. The T-RANS results confirmed a good agreement with the mean streamwise velocity and turbulent kinetic energy, as well as the mean temperature profiles. Finally, the analysis of power spectral density distributions of the temperature signal revealed that all simulation techniques captured a dominant flow frequency originating from the induced Kelvin-Helmholtz instabilities between the side and central jets. The presented combined dynamic LES/DNS approach is recommended for future simulations of the turbulent forced convection flows of low Prandtl fluids, especially if thermal fatigue effects need to be predicted correctly.

The present study addresses numerical simulations of the double-diffusive convection in a turbulent regime. The characteristic concentration and temperature Prandtl numbers (PrC = 700 and PrT = 7) correspond to the typical seawater properties. Here, we applied the Large-Eddy Simulation (LES) approach, with relatively simple subgrid turbulence closures of momentum, concentration, and temperature for the unresolved scales. The wall-resolved LES approach proved to work well on significantly coarser numerical mesh than used in recent Direct Numerical Simulation (DNS) studies of Yang et al. (2016) in the intermediate range of working parameters, 107 ≤ RaC ≤ 109, 0 ≤ RaT ≤ 106, which covers the quasi-Rayleigh-Bénard, fingering, and damping flow regimes. A good agreement between concentration and temperature Nusselt numbers is obtained. The instantaneous Nusselt numbers distribution revealed a significant impact of the imposed strong thermal stratification (RaT = 106) in comparison to the neutral case (RaT = 0).

Background
Properly 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.

Methods
We 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.

Results
The 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).

Conclusions
In 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.

A long-time exposure to lack of oxygen (hypoxia) in some regions of the cerebrovascular system is believed to be one of the causes of cerebral neurological diseases. In the present study, we show how a combination of magnetic resonance imaging (MRI) and computational fluid dynamics (CFD) can provide a noninvasive alternative for studying blood flow and transport of oxygen within the cerebral vasculature. We perform computer simulations of oxygen mass transfer in the subject-specific geometry of the circle of Willis. The computational domain and boundary conditions are based on four-dimensional (4D)-flow MRI measurements. Two different oxygen mass transfer models are considered: passive (where oxygen is treated as a dilute chemical species in plasma) and active (where oxygen is bonded to haemoglobin) models. We show that neglecting haemoglobin transport results in a significant underestimation of the arterial wall mass transfer of oxygen. We identified the hypoxic regions along the arterial walls by introducing the critical thresholds that are obtained by comparison of the estimated range of Damköhler number (Da, 〈9; 57〉) with the local Sherwood number. Finally, we recommend additional validations of the combined MRI/CFD approach proposed here for larger groups of subject- or patient-specific brain vasculature systems.

Purpose: Intraventricular blood flow dynamics are associated with cardiac function. Accurate, noninvasive, and easy assessments of hemodynamic quantities (such as velocity, vortex, and pressure) could be an important addition to the clinical diagnosis and treatment of heart diseases. However, the complex time-varying flow brings many challenges to the existing noninvasive image-based hemodynamic assessments. The development of reliable techniques and analysis tools is essential for the application of hemodynamic biomarkers in clinical practice. Methods: In this study, a time-resolved particle tracking method, Shake-the-Box, was applied to reconstruct the flow in a realistic left ventricle (LV) silicone model with biological valves. Based on the obtained velocity, 4D pressure field was calculated using a Poisson equation-based pressure solver. Furthermore, flow analysis by proper orthogonal decomposition (POD) of the 4D velocity field has been performed. Results: As a result of the Shake-the-Box algorithm, we have extracted: (i) particle positions, (ii) particle tracks, and finally, (iii) 4D velocity fields. From the latter, the temporal evolution of the 3D pressure field during the full cardiac cycle was obtained. The obtained maximal pressure difference extracted along the base-to-apex was about 2.7 mmHg, which is in good agreement with those reported in vivo. The POD analysis results showed a clear picture of different scale of vortices in the pulsatile LV flow, together with their time-varying information and corresponding kinetic energy content. To reconstruct 95% of the kinetic energy of the LV flow, only the first six POD modes would be required, leading to significant data reduction. Conclusions: This work demonstrated Shake-the-Box is a promising technique to accurately reconstruct the left ventricle flow field in vitro. The good spatial and temporal resolutions of the velocity measurements enabled a 4D reconstruction of the pressure field in the left ventricle. The application of POD analysis showed its potential in reducing the complexity of the high-resolution left ventricle flow measurements. For future work, image analysis, multi-modality flow assessments, and the development of new flow-derived biomarkers can benefit from fast and data-reducing POD analysis.

OBJECTIVES: To date, it is not known if 16–20-mm extracardiac conduits are outgrown during somatic growth from childhood to adolescence. This study aims to determine total cavopulmonary connection (TCPC) haemodynamics in adolescent Fontan patients at rest and during simulated exercise and to assess the relationship between conduit size and haemodynamics. METHODS: Patient-specific, magnetic resonance imaging-based computational fluid dynamic models of the TCPC were performed in 51 extracardiac Fontan patients with 16–20-mm conduits. Power loss, pressure gradient and normalized resistance were quantified in rest and during simulated exercise. The cross-sectional area (CSA) (mean and minimum) of the vessels of the TCPC was determined and normalized for flow rate (mm²/l/min). Peak (predicted) oxygen uptake was assessed. RESULTS: The median age was 16.2 years (Q1–Q3 14.0–18.2). The normalized mean conduit CSA was 35–73% smaller compared to the inferior and superior vena cava, hepatic veins and left/right pulmonary artery (all P < 0.001). The median TCPC pressure gradient was 0.7 mmHg (Q1–Q3 0.5–0.8) and 2.0 (Q1–Q3 1.4–2.6) during rest and simulated exercise, respectively. A moderate–strong inverse nonlinear relationship was present between normalized mean conduit CSA and TCPC haemodynamics in rest and exercise. TCPC pressure gradients of >_1.0 at rest and >_3.0 mmHg during simulated exercise were observed in patients with a conduit CSA <_ 45 mm²/l/min and favourable haemodynamics (<1 mmHg during both rest and exercise) in conduits >_125 mm²/l/min. Normalized TCPC resistance correlated with (predicted) peak oxygen uptake. CONCLUSIONS: Extracardiac conduits of 16–20 mm have become relatively undersized in most adolescent Fontan patients leading to suboptimal haemodynamics.

There is a pressing need to establish novel biomarkers to predict the progression of thoracic aortic aneurysm (TAA) dilatation. Aside from hemodynamics, the roles of oxygen (O₂) and nitric oxide (NO) in TAA pathogenesis are potentially significant. As such, it is imperative to comprehend the relationship between aneurysm presence and species distribution in both the lumen and aortic wall. Given the limitations of existing imaging methods, we propose the use of patient-specific computational fluid dynamics (CFD) to explore this relationship. We have performed CFD simulations of O₂ and NO mass transfer in the lumen and aortic wall for two cases: a healthy control (HC) and a patient with TAA, both acquired using 4D-flow magnetic resonance imaging (MRI). The mass transfer of O₂ was based on active transport by hemoglobin, while the local variations of the wall shear stress (WSS) drove NO production. Comparing hemodynamic properties, the time-averaged WSS was considerably lower for TAA, while the oscillatory shear index and endothelial cell activation potential were notably elevated. O₂ and NO showed a non-uniform distribution within the lumen and an inverse correlation between the two species. We identified several locations of hypoxic regions for both cases due to lumen-side mass transfer limitations. In the wall, NO varied spatially, with a clear distinction between TAA and HC. In conclusion, the hemodynamics and mass transfer of NO in the aorta exhibit the potential to serve as a diagnostic biomarker for TAA. Furthermore, hypoxia may provide additional insights into the onset of other aortic pathologies.