Search Results (4)

This paper presents a case study comparing CFD predictions with experimental measurements for micropumps, with the goal of evaluating the accuracy and limitations of CFD methods in complex microscale geometries. A fast design and evaluation methodology was developed, integrating linear design, 3D fully viscous CFD-based optimization, and rapid prototyping and testing. The main problem at this scale and configuration of pumps is the combination of Reynolds and Taylor numbers. Their impact on labyrinth performance prediction and therefore volumetric efficiency dominates the losses at this scale. Multiple CFD simulations were conducted using various turbulence models and solver settings, and results were compared against experimental data. The labyrinth region was simulated both independently and as part of the full pump assembly, with RANS and LES used for the former and RANS for the latter. Precision 3D-printed rotors and volutes were tested, and performance maps were obtained. Significant discrepancies between CFD and experiments were observed, which were reconciled using two empirical scaling coefficients for pressure and mass flow. These collapsed the CFD predictions onto the experimental data across all available speedlines. While the generalizability of these coefficients remains uncertain, the concept of using corrected scales, rather than other methods, seems to capture the macroscopic discrepancies between CFD and experiments. Full article

Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems. Full article

Accurate prediction of extreme atmospheric conditions is essential for various scientific and engineering applications, ranging from environmental monitoring to space weather forecasting and urban climate resilience. This study introduces an empirical approach to predict maximum atmospheric pressure and temperature using an empirical model based on statistical parameters. The model incorporates key inputs such as the mean value, standard deviation, integral time scale, and a variability factor, denoted as b, to capture application-specific uncertainties. The methodology is applied to two distinct atmospheric scenarios: (i) forecasting maximum atmospheric pressure using data from 29 global monitoring stations, and (ii) predicting maximum temperature around isolated structures within unstable boundary layers, leveraging insights from Large Eddy Simulation (LES) data. The results indicate that the model performs robustly across diverse conditions, with the b parameter exhibiting a wide range of values depending on the specific atmospheric setting. The comparison between model predictions and observed data demonstrates excellent agreement, validating the model’s applicability in extreme value prediction. These findings reinforce the empirical model’s potential for integration into computational fluid dynamics (CFD) simulations, enhancing the predictive capabilities of Reynolds-Averaged Navier-Stokes (RANS) methodologies. Furthermore, the model’s ability to generalize across different atmospheric processes highlights its significance in advancing our understanding of meteorological extremes. Full article

In today’s road vehicle design processes, Computational Fluid Dynamics (CFD) has emerged as one of the major investigative tools for aerodynamics analyses. The age-old CFD methodology based on the Reynolds Averaged Navier–Stokes (RANS) approach is still considered as the most popular turbulence modeling approach in automotive industries due to its acceptable accuracy and affordable computational cost for predicting flows involving complex geometries. This popular use of RANS still persists in spite of the well-known fact that, for automotive flows, RANS turbulence models often fail to characterize the associated flow-field properly. It is even true that more often, the RANS approach fails to predict correct integral aerodynamic quantities like lift, drag, or moment coefficients, and as such, they are used to assess the relative magnitude and direction of a trend. Moreover, even for such purposes, notable disagreements generally exist between results predicted by different RANS models. Thanks to fast advances in computer technology, increasing popularity has been seen in the use of the hybrid Detached Eddy Simulation (DES), which blends the RANS approach with Large Eddy Simulation (LES). The DES methodology demonstrated a high potential of being more accurate and informative than the RANS approaches. Whilst evaluations of RANS and DES models on various applications are abundant in the literature, such evaluations on full-car models are relatively fewer. In this study, four RANS models that are widely used in engineering applications, i.e., the realizable $k-\epsilon$ two-layer, Abe–Kondoh–Nagano (AKN) $k-\epsilon$ low-Reynolds, SST $k-\omega$ , and V2F are evaluated on a full-scale passenger vehicle with two different front-end configurations. In addition, both cases are run with two DES models to assess the differences between the flow predictions obtained using RANS and DES. Full article