Background: The utilization of hemodynamic parameters, whose estimation is often cumbersome, can fasten diagnostics and decision-making related to congenital heart diseases. The main goal of this study is to investigate the relationship between hemodynamic and morphometric features of the thoracic aorta and to construct corresponding predictive models. Methods: Multi-slice spiral computed tomography images of the aortas of patients with coarctation diagnoses and patients without cardiac or vascular diseases were evaluated to obtain numerical models of the aorta and branches of the aortic arch. Hemodynamic characteristics were estimated in key subdomains of the aorta and three branches using computational fluid dynamics methods. The key morphometric features (diameters) were calculated at locations in proximity to the domains, where hemodynamic characteristics are evaluated. Results: The functional dependencies for velocities and pressure on the corresponding diameters have been fitted, and a metamodel has been constructed employing the predicted values from these models. Conclusions: The metamodel demonstrated high accuracy in classifying aortas into their respective types, thereby confirming the adequacy of the predicted hemodynamic characteristics by morphometric characteristics. The proposed methodology is applicable to other heart diseases without fundamental changes.

Coarctation of the aorta is a localized narrowing of the aortic lumen. This pathology leads to hypertension in upper extremity vessels, left ventricular hypertrophy and to impaired perfusion of the abdominal cavity and lower extremities. Along with traditional diagnostic methods, mathematical modeling is used for risk assessment and the prediction of disease outcomes. However, when applying numerical models to describe hemodynamic parameters, the choice of turbulence model to describe swirling flow occurring in the aorta in this pathology must be justified. Thus, three turbulence models, namely k-ε, k-ω, and SST were analyzed for the description of swirling flows in the study of coarctation’s effect on hemodynamic parameters and analysis of the mechanisms leading to various cardiovascular diseases caused by altered hemodynamics. The results revealed significant differences in swirling flow patterns between the k-ε and k-ω models, while the k-ω and SST models showed consistent results over the cardiac cycle. In the peak systolic phase, average velocity rises to 1.07–1.98 m·s⁻¹ for the k-ε model, 0.82–2.12 m·s⁻¹ for the k-ω model, 1.22–2.12 m·s⁻¹ for the SST model and 0.8–2.12 m·s⁻¹ for laminar flow. WSS values increase rapidly to 11–22 Pa in k-ε, 25–50 Pa in k-ω and SST models of turbulence, and 30–55 Pa for laminar flow. Significant differences were also evident in the prediction of wall shear stress, with the k-ε model giving values more than twice as high as the k-ω and SST models. The data obtained confirm the necessity of careful model selection for accurate hemodynamic parameter estimation, especially in coarctation. The findings of this study can be used for further physics-informed neural network analysis of evaluation of treatment evaluations for congenital heart disease patients.

Over the past decade, the development of accurate turbulence models to describe blood flow in the aorta for cardiovascular applications has increased substantially, leading to growing efforts to quantify turbulence‐related hemodynamic parameters and improved model fidelity. At the same time, a unified computational standard, including for clinical use, remains absent. Moreover, there is still no unified standard for modeling turbulent flows in the aorta, and the choice of turbulence model, boundary conditions, and validation. In this paper, a systematic review of the simulation studies of turbulent blood flow in the aorta. Scopus, PubMed, and ScienceDirect databases were used in the literature search, which was completed on the 9th of July 2026. 38 articles were included after the selection process. The data from the research works were structured in different categories (Geometry, Viscosity model, Model turbulence, Parameters, Validation). The aim of this systematic review is to systematize the methodological approaches used in the state‐of‐the‐art literature, including the choice of turbulence models, validation methods, as well as the identification of the main hemodynamic parameters characterizing turbulent flow.