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The growing interest in renewable energy solutions for sustainable development has significantly advanced the design and analysis of floating offshore wind turbines (FOWTs). Modeling FOWTs presents challenges due to the considerable coupling between the turbine’s aerodynamics and the floating platform’s hydrodynamics. This review paper highlights the critical role of computational fluid dynamics (CFD) in enhancing the design and performance evaluation of FOWTs. It thoroughly evaluates various CFD approaches, including uncoupled, partially coupled, and fully coupled models, to address the intricate interactions between aerodynamics, hydrodynamics, and structural dynamics within FOWTs. Additionally, this paper reviews a range of software tools for FOWT numerical analysis. The research emphasizes the need to focus on the coupled aero-hydro-elastic models of FOWTs, especially in response to expanding rotor diameters. Further research should focus on developing nonlinear eddy viscosity models, refining grid techniques, and enhancing simulations for realistic sea states and wake interactions in floating wind farms. The research aims to familiarize new researchers with essential aspects of CFD simulations for FOWTs and to provide recommendations for addressing challenges.

According to the structure of the air channel in the drying chamber of an automobile factory, a model of the original structure of the drying chamber was established. The air flow of this structure is analyzed based on the CFD simulation, and the velocity at specific points of the car shell being dried is also analyzed. The results show that the original drying chamber structure is optimized, the air flow is uniform in the drying air chamber and short circuit of air between inlet and outlet in the drying chamber is decreased.

Racecar aerodynamic development requires well-correlated simulation data for rapid and incremental development cycles. Computational Fluid Dynamics (CFD) simulations and wind tunnel testing are industry-wide tools to perform such development, and the best use of these tools can define a race team’s ability to compete. With CFD usage being limited by the sanctioning bodies, large-scale mesh and large-time-step CFD simulations based on Reynolds-Averaged Navier–Stokes (RANS) approaches are popular. In order to provide the necessary aerodynamic performance advantages sought by CFD development, increasing confidence in the validity of CFD simulations is required. A previous study on a Scale-Averaged Simulation (SAS) approach using RANS simulations of a Gen-6 NASCAR, validated against moving-ground, open-jet wind tunnel data at multiple configurations, produced a framework with good wind tunnel correlation (within 2%) in aerodynamic coefficients of lift and drag predictions, but significant error in front-to-rear downforce balance (negative lift) predictions. A subsequent author’s publication on a Scale-Resolved Simulation (SRS) approach using Improved Delayed Detached Eddy Simulation (IDDES) for the same geometry showed a good correlation in front-to-rear downforce balance, but lift and drag were overpredicted relative to wind tunnel data. The current study compares the surface pressure distribution collected from a full-scale wind tunnel test on a Gen-6 NASCAR to the SAS and SRS predictions (both utilizing SST k−ω turbulence models). CFD simulations were performed with a finite-volume commercial CFD code, Star-CCM+ by Siemens, utilizing a high-resolution CAD model of the same vehicle. A direct comparison of the surface pressure distributions from the wind tunnel and CFD data clearly showed regions of high and low correlations. The associated flow features were studied to further explore the strengths and areas of improvement needed in the CFD predictions. While RANS was seen to be more accurate in terms of lift and drag, it was a result of the cancellation of positive and negative errors. Whereas IDDES overpredicted lift and drag and requires an order of magnitude more computational resources, it was able to capture the trend of surface pressure seen in the wind tunnel measurements.

A Machine Learning-Genetic Algorithm (ML-GA) approach was developed to virtually discover optimum designs using training data generated from multi-dimensional simulations. Machine learning (ML) presents a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. In the present work, a total of over 2000 sector-mesh computational fluid dynamics (CFD) simulations of a heavy-duty engine were performed. These were run concurrently on a supercomputer to reduce overall turnaround time. The engine being optimized was run on a low-octane (RON70) gasoline fuel under partially premixed compression ignition (PPCI) mode. A total of nine input parameters were varied, and the CFD simulation cases were generated by randomly sampling points from this nine-dimensional input space. These input parameters included fuel injection strategy, injector design, and various in-cylinder flow and thermodynamic conditions at intake valve closure (IVC). The outputs (targets) of interest from these simulations included five metrics related to engine performance and emissions. Over 2000 samples generated from CFD were then used to train an ML model that could predict these five targets based on the nine input features. A robust super learner approach was employed to build the ML model, where results from a collection of different ML algorithms were pooled together. Thereafter, a stochastic global optimization genetic algorithm (GA) was used, with the ML model as the objective function, to optimize the input parameters based on a merit function so as to minimize fuel consumption while satisfying CO and NOx emissions constraints. The optimized configuration from ML-GA was found to be very close to that obtained from a sequentially performed CFD-GA approach, where a CFD simulation served as the objective function. In addition, the overall turnaround time was (at least) 75% lower with the ML-GA approach, as the training data was generated from concurrent CFD simulations and employing the ML model as the objective function significantly accelerated the GA optimization. This study demonstrates the potential of ML-GA and high-performance computing (HPC) to reduce the number of CFD simulations to be performed for optimization problems without loss in accuracy, thereby providing significant cost savings compared to traditional approaches.

The engineering discipline of in silico fluid dynamics delivers quantitative information on airflow behaviour in the nasal regions with unprecedented detail, often beyond the reach of traditional experiments. The ability to provide visualisation and analysis of flow properties such as velocity and pressure fields, as well as wall shear stress, dynamically during the respiratory cycle may give significant insight to clinicians. Yet, there remains ongoing challenges to advance the state-of-the-art further, including for example the lack of comprehensive CFD modelling on varied cohorts of patients. The present article embodies a review of previous and current in silico approaches to simulating nasal airflows. The review discusses specific modelling techniques required to accommodate physiologically- and clinically-relevant findings. It also provides a critical summary of the reported results in the literature followed by an outlook on the challenges and topics anticipated to drive research into the future.

The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard k−ω SST turbulence model.

Leone, E.; Francone, A.; Paglialunga, A.; Ciardulli, F.; Aloisi, A., and Tomasicchio, G.R., 2024. Overtopping assessment of a rubble mound breakwater with innovative armor units: a physical and numerical study. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 804-808. Charlotte (North Carolina), ISSN 0749-0208. Rubble-mound breakwaters are essential for coastal defense, protecting ports and mitigating erosion. During storms, water overflow can cause circulating currents in protected zones. Integrating innovative armor units that efficiently dissipate energy is key in reducing wave overtopping. An experimental investigation has been conducted on a scaled model of a rubble-mound breakwater with innovative armor units at the EUMER (EUropean Maritime Environmental Research) laboratory, University of Salento, Italy. The model replicated a defense structure in the Arabian Gulf, protecting an artificial island. A critical section prone to overtopping has been built at a 1:15 model scale. The investigation analyzed the performance of the armor units in terms of wave overtopping under operational and extreme conditions, considering the Gulf's wave characteristics. To enhance reliability, the physical model investigation has been complemented by a numerical study. Numerical simulations have been performed using the OpenFoam C++ libraries and the IHFOAM multiphase flow solver. The VARANS equations solved the two-phase flow within the breakwater's porous media. To close the system of equations describing the turbulent flow, the k-ω SST turbulence model has been selected, due to the good trade-off between cost and accuracy. The VOF method has been applied to track the free surface elevation over time. The numerical simulations showed strong agreement with experimental observations, demonstrating IHFOAM as a reliable tool to predict wave overtopping phenomena.

Flow field design has an important role in proton exchange membrane fuel cell (PEMFC) due to its effect on the distribution of pressure, current density, temperature, heat and water management and PEMFC performance. In this paper, the sinusoidal flow field is examined and compared with straight-parallel configuration using a finite volume method based on non-isothermal, steady-state and multiphase model. A set of continuity, momentum, energy, species and electrochemical equations is solved by CFD commercial code with SIMPLE algorithm as a solution approach. The obtained results reveal that at an operating voltage, the maximum velocity and pressure drop for sinusoidal flow field are 1.18 and 6 times more than straight-parallel flow field at GDL/CL interface. Also, it is found that the current density and maximum power density for sinusoidal flow field are 0.65 and 0.32 w cm −2 , respectively. Ultimately, the results indicated that the sinusoidal flow field has better performance in compared with straight-parallel flow field.

In our latest research, we investigated the incorporation of an undulatory pattern along the concave surface of Savonius turbine blades. This study builds upon our previous findings, where we examined the effects of varying undulation counts across four distinct shapes (with radius of 22 mm, 30 mm, 60 mm, and 80 mm), while maintaining consistent overall blade dimensions. To analyze the aerodynamic performance, we employed the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with the Shear Stress Transport (SST) k-ω turbulence model, using ANSYS Fluent software. In the current study, the number of undulations was increased by approximately 50%, resulting in a total of 60 points along the blade profile. This enhanced configuration was validated using recent numerical data to assess its impact on the moment and power coefficients of the turbine across a range of tip speed ratios (TSRs). All simulations were conducted under consistent conditions, including a 15% overlap ratio relative to the rotor diameter and an inlet wind velocity of 7 m/s. This study shows that the MODEL80 exhibited the highest power coefficient value of 0.277.

This study analyzes the thermal performance of a 4.1 dm³ engine radiator through experimental tests and CFD simulations using ANSYS Fluent. The effects of materials, tube geometry, and flow conditions on heat transfer and thermal efficiency were evaluated. The results show that copper tubes enhance heat transfer by 18% but increase pressure drop by 4.44%. Additionally, increasing air velocity improves thermal efficiency by 3.74%, suggesting that specific improvements in fin design could enhance performance without increasing energy consumption. The study validates the use of CFD as a reliable tool for analyzing cooling systems in engines, benefiting the automotive industry with more efficient radiators. These improvements can be extended to hybrid and electric vehicles, as well as industrial heat exchangers, contributing to more sustainable thermal management. The main scientific contributions of this work are: (i) the experimental validation of a CFD model applied to an automotive radiator under transitional flow regime, (ii) the quantitative evaluation of the effects of copper tubes on thermal efficiency and pressure drop, and (iii) the detailed analysis of air velocity impact on heat transfer and its implications for radiator thermal design. Este estudio analiza el rendimiento térmico de un radiador de motor de 4.1 dm³ mediante pruebas experimentales y simulaciones CFD en ANSYS Fluent. Se evaluaron los efectos de materiales, geometría de tubos y condiciones de flujo en la transferencia de calor y eficiencia térmica. Los resultados muestran que los tubos de cobre mejoran la transferencia de calor en un 18%, pero aumentan la caída de presión en un 4.44%. Además, incrementar la velocidad del aire mejora la eficiencia térmica en un 3.74%, lo que sugiere que ciertas mejoras en el diseño de las aletas podrían aumentar el desempeño sin afectar el consumo energético. El estudio valida el uso de CFD como herramienta confiable para el análisis de sistemas de enfriamiento en motores, beneficiando a la industria automotriz con radiadores más eficientes. Estas mejoras pueden extenderse a vehículos híbridos y eléctricos, así como a intercambiadores de calor industriales, contribuyendo a una gestión térmica más sostenible. Las principales contribuciones científicas de este trabajo son: (i) la validación experimental de un modelo CFD aplicado a un radiador automotriz en régimen de flujo transitorio, (ii) la evaluación cuantitativa del efecto de los tubos de cobre sobre la eficiencia térmica y la caída de presión, y (iii) el análisis detallado del impacto de la velocidad del aire en la transferencia de calor y sus implicaciones en el diseño térmico del radiador.