An official website of the United States government
We introduce a wall model for large-eddy simulation (WMLES) applicable to rough surfaces with Gaussian and non-Gaussian distributions for both the transitionally and fully rough regimes. The model is applicable to arbitrary complex geometries where roughness elements are assumed to be underresolved, i.e. subgrid-scale roughness. The wall model is implemented using a multi-hidden-layer feedforward neural network, with the mean geometric properties of the roughness topology and near-wall flow quantities serving as input. The optimal set of non-dimensional input features is identified using information theory, selecting variables that maximize information about the output while minimizing redundancy among inputs. The model also incorporates a confidence score based on Gaussian process modelling, enabling the detection of potentially low model performance for untrained rough surfaces. The model is trained using a direct numerical simulation (DNS) roughness database comprising approximately 200 cases. The roughness geometries for the database are selected from a large repository through active learning. This approach ensures that the rough surfaces incorporated into the database are the most informative, achieving higher model performance with fewer DNS cases compared with passive learning techniques. The performance of the model is evaluated bothaprioriandaposterioriin WMLES of turbulent channel flows with rough walls. Over 550 channel flow cases are considered, including untrained roughness geometries, roughness Reynolds numbers and grid resolutions for both transitionally and fully rough regimes. Our rough-wall model offers higher accuracy than existing models, generally predicting wall shear stress within an accuracy range of 1%–15 %. The performance of the model is also assessed on a high-pressure turbine blade with two different rough surfaces. We show that the new wall model predicts the skin friction and the mean velocity deficit induced by the rough surface on the blade within 1%–10 % accuracy except the region with transition or shock waves. This work extends the building-block flow wall model (BFWM) introduced by Lozano-Durán & Bae (2023.J. Fluid Mech.963, A35) for smooth walls, expanding the BFWM framework to account for rough-wall scenarios.
more »
« less
In Search of a Universal Rough Wall Model
Abstract This work compares various existing rough-wall models on a large collection of rough surfaces with different characteristics and studies the potential of these models in accommodating new datasets. We consider three empirical roughness correlations, two physics-based models, and one data-driven machine-learning model on 68 rough surfaces inside and outside the Roughness Database1. Results show that correlation-type models and machine-learning models do not extrapolate outside the dataset against which they are calibrated or trained. In contrast, the physics-based sheltering model performs well in extrapolation. Recalibrating a roughness correlation against a large dataset proves unfruitful. However, retraining a machine learning model yields good results. We do not pursue further retraining and recalibrating of a physics-based model, as it requires new physical insights. Overall, our findings suggest that a universal rough-wall model is yet to be found. The capability of extrapolation will likely come from incorporating physics. Data, on the other hand, benefits machine learning models.
more »
« less
- Award ID(s):
- 2231037
- PAR ID:
- 10474227
- Publisher / Repository:
- ASME
- Date Published:
- Journal Name:
- Journal of Fluids Engineering
- Volume:
- 145
- Issue:
- 10
- ISSN:
- 0098-2202
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Turbulent flows over rough surfaces can be encountered in a wide range of engineering applications. Despite the progress made after several decades of studies, the prediction of drag and roughness function from the surface geometrical parameters remains an open question. Several methods have shown encouraging results. However, they lack generality and present some scatter in the data. In this paper we propose a new parameter, the effective distribution ($$ED$$), which lays foundation on the effective slope with some changes to take into account the sheltering effect of large roughness elements and the drag induced by pinnacles higher than the average roughness elements. To develop this new correlation between geometrical features of the wall and the drag, we performed a set of simulations of the turbulent flow over a rough surface made of triangular elements varying their height and spatial distribution. The$$ED$$correlates quite well both with the drag and the roughness function for a wide range of cases having different mean roughness height, skewness and kurtosis. To further validate the$$ED$$, and assessing how it can be generalized to real rough wall, an irregular wall made from the superposition of random sinusoidal function was considered. Results were consistent with the correlation here presented.more » « less
-
Outside laboratory conditions and human-made structures, animals rarely encounter flat surfaces. Instead, natural substrates are uneven surfaces with height variation that ranges from the microscopic scale to the macroscopic scale. For walking animals (which we define as encompassing any form of legged movement across the ground, such as walking, running, galloping, etc.), such substrate ‘roughness’ influences locomotion in a multitude of ways across scales, from roughness that influences how each toe or foot contacts the ground, to larger obstacles that animals must move over or navigate around. Historically, the unpredictability and variability of natural environments has limited the ability to collect data on animal walking biomechanics. However, recent technical advances, such as more sensitive and portable cameras, biologgers, laboratory tools to fabricate rough terrain, as well as the ability to efficiently store and analyze large variable datasets, have expanded the opportunity to study how animals move under naturalistic conditions. As more researchers endeavor to assess walking over rough terrain, we lack a consistent approach to quantifying roughness and contextualizing these findings. This Review summarizes existing literature that examines non-human animals walking on rough terrain and presents a metric for characterizing the relative substrate roughness compared with animal size. This framework can be applied across terrain and body scales, facilitating direct comparisons of walking over rough surfaces in animals ranging in size from ants to elephants.more » « less
-
Aiming to study the rough-wall turbulent boundary layer structure over differently arranged roughness elements, an experimental study was conducted on flows with regular and random roughness. Varying planform densities of truncated cone roughness elements in a square staggered pattern were investigated. The same planform densities were also investigated in random arrangements. Velocity statistics were measured via two-component laser Doppler velocimetry and stereoscopic particle image velocimetry. Friction velocity, thickness, roughness length and zero-plane displacement, determined from spatially averaged flow statistics, showed only minor differences between the regular and random arrangements at the same density. Recent a priori morphometric and statistical drag prediction methods were evaluated against experimentally determined roughness length. Observed differences between regular and random surface flow parameters were due to the presence of secondary flows which manifest as high-momentum pathways and low-momentum pathways in the streamwise velocity. Contrary to expectation, these secondary flows were present over the random surfaces and not discernible over the regular surfaces. Previously identified streamwise-coherent spanwise roughness heterogeneity does not seem to be present, suggesting that such roughness heterogeneity is not necessary to sustain secondary flows. Evidence suggests that the observed secondary flows were initiated at the front edge of the roughness and sustained over irregular roughness. Due to the secondary flows, local turbulent boundary layer profiles do not scale with local wall shear stress but appear to scale with local turbulent shear stress above the roughness canopy. Additionally, quadrant analysis shows distinct changes in the populations of ejection and sweep events.more » « less
-
Abstract Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4062820
