arXiv subjects

Konrad Goc

Publications and source records attributed to Konrad Goc.

5 recordsLinked to original sources

arXiv2026

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics

This paper describes the first-ever open-source high-fidelity CFD dataset of a high-lift aircraft for the purpose of AI surrogate model development. The dataset is composed of 1800 samples, arising from 180 geometry variants and 10 angles of attack for the high-lift NASA Common Research Model (CRM) geometry, used within the AIAA High-Lift Prediction Workshop series. One of the novelties of this dataset is the use of a GPU-accelerated high-fidelity explicit, wall-modeled LES approach for each simulation, using solution-adapted grids between 300M and 500M cells. This ensures the greatest possible accuracy given known challenges in steady-state RANS approaches for these portions of the flight envelope. The entire dataset (geometries, time-averaged volume and surface variables and integral forces) are available, free of charge with a permissive open-source license (CC-BY-4.0). By making this data publicly available, we aim to accelerate the research and development of AI surrogate modeling within the aerospace industry.

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arXiv2025

General-purpose Data-driven Wall Model for Low-speed Flows Part I: Baseline Model

We present a general-purpose wall model for large-eddy simulation. The model builds on the building-block flow principle, leveraging essential physics from simple flows to train a generalizable model applicable across complex geometries and flow conditions. The model addresses key limitations of traditional equilibrium wall models (EQWM) and improves upon shortcomings of earlier building-block-based approaches. The model comprises four components: (i) a baseline wall model, (ii) an error model, (iii) a classifier, and (iv) a confidence score. The baseline model predicts the wall-shear stress, while the error model estimates epistemic errors and aleatoric errors, both used for uncertainty quantification. In Part I of this work, we present the baseline model, while the remaining three components are introduced in Part II. The baseline model is designed to capture a broad range of flow phenomena, including turbulence over curved walls and zero, adverse, and favorable mean pressure gradients, as well as flow separation and laminar flow. The problem is formulated as a regression task to predict wall shear stress using a neural network. Model inputs are localized in space and dimensionless, with their selection guided by information-theoretic criteria. Training data include, among other cases, a newly generated direct numerical simulation dataset of turbulent boundary layers under favorable and adverse PG conditions. Validation is carried out through both a priori and a posteriori tests. The a priori evaluation spans 140 diverse high-fidelity numerical datasets and experiments (67 training cases included), covering turbulent boundary layers, airfoils, Gaussian bumps, and full aircraft geometries, among others. We demonstrate that the baseline wall model outperforms the EQWM in 90% of test scenarios, while maintaining errors below 20% for 98% of the cases.

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arXiv2024

Building-block flow model for computational fluids

We introduce a closure model for wall-modeled large-eddy simulation (WMLES), referred to as the Building-block Flow Model (BFM). The foundation of the model rests on the premise that a finite collection of simple flows encapsulates the essential physics necessary to predict more complex scenarios. The BFM is implemented using artificial neural networks and introduces five advancements within the framework of WMLES: (1) It is designed to predict multiple flow regimes (wall turbulence under zero, favorable, adverse mean-pressure-gradient, and separation); (2) It unifies the closure model at solid boundaries (i.e., the wall model) and the rest of the flow (i.e., the subgrid-scale model) into a single entity; (3) It ensures consistency with numerical schemes and gridding strategy by accounting for numerical errors; (4) It is directly applicable to arbitrary complex geometries; (5) It can be scaled up to model additional flow physics in the future if needed (e.g., shockwaves and laminar-to-turbulent transition). The BFM is utilized to predict key quantities of interest in turbulent channel and pipe flows, a Gaussian bump, a simplified aircraft, and a realistic aircraft in landing configuration. In all cases, the BFM demonstrates similar or superior capabilities in terms of accuracy and computational efficiency compared to previous state-of-the-art closure models.

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arXiv2023

Large-eddy simulations of the NACA23012 airfoil with laser-scanned ice shapes

In this study, five ice shapes generated at NASA Glenn's Icing Research Tunnel (IRT) are simulated at multiple angles of attack (Broeren et al., J. of Aircraft, 2018). These geometries target different icing environments, both early-time and longer-duration glaze and rime ice exposure events, including a geometry that results from using a thermal ice-protection system. Using the laser-scanned geometries, detailed representations of the three-dimensional ice geometries are resolved on the grid and simulated using wall-modeled LES. Integrated loads (lift, drag, and moment coefficients) and pressure distributions are compared against experimental measurements in both clean and iced conditions for several angles of attack in both pre-and post-stall regions. The relevant comparisons to the experimental results show that qualitative and acceptable quantitative agreement with the data is observed across all geometries. Glaze ice formations exhibit larger and highly nonuniform ice features, such as `horns', in contrast to rime ice formations characterized by smaller, uniformly distributed roughness elements. In wall-modeled LES, it was observed that larger roughness scales in the glaze ice that trigger transition can be accurately resolved. Therefore, it is possible for WMLES to accurately capture the aerodynamics of glaze ice shapes without the need for additional modeling. In contrast, rime ice geometries required additional resolution to accurately represent the aerodynamic loads. This study demonstrates the effectiveness of the wall-modeled LES technique in simulating the complex aerodynamic effects of iced airfoils, providing valuable insights for aircraft design in icing environments and highlighting the importance of accurately representing ice geometries and roughness scales in simulations.

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arXiv2022

Wall-modeled large-eddy simulation based on building-block flows

A unified subgrid-scale (SGS) and wall model for large-eddy simulation (LES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that simple canonical flows contain the essential physics to provide accurate predictions of the SGS tensor in more complex flows. The model is constructed to predict zero-pressure-gradient wall-bounded turbulence, adverse pressure gradient effects, separation and laminar flow. The approach is implemented using a Bayesian classifier, which identifies the contribution of each building block in the flow, and a neural-network-based predictor, which estimates the eddy viscosity based on the building-block units. The training data are directly obtained from wall-modeled LES with an exact SGS/wall model for the mean quantities to guarantee consistency with the numerical discretization. The model is validated in canonical flows and the NASA High-Lift Common Research Model and shown to improve the predictions with respect to current modeling approaches.

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