An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression

• URL: http://arxiv.org/abs/2501.08134v1
• Date: Tue, 14 Jan 2025 14:14:22 GMT
• Title: An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression
• Authors: Laura Botero Bolívar, David Huergo, Fernanda L. dos Santos, Cornelis H. Venner, Leandro D. de Santana, Esteban Ferrer,
• Abstract summary: This paper introduces a new wall-pressure spectrum empirical model designed to enhance the robustness and accuracy of current predictions. The model is developed using AI-based symbolic regression via a genetic-algorithm-based approach. It is integrated with Amiet's theory to predict the aeroacoustic noise of a full-scale wind turbine.
• Score: 36.814181034608666
• License:
• Abstract: Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these aeroacoustic predictive models, Amiet's theory offers the best balance between accuracy and simplicity. The accuracy of the model relies heavily on precise wall-pressure spectrum predictions, which are often based on single-equation formulations with adjustable parameters. These parameters are calibrated for particular airfoils and flow conditions and consequently tend to fail when applied outside their calibration range. This paper introduces a new wall-pressure spectrum empirical model designed to enhance the robustness and accuracy of current state-of-the-art predictions while widening the range of applicability of the model to different airfoils and flow conditions. The model is developed using AI-based symbolic regression via a genetic-algorithm-based approach, and applied to a dataset of wall-pressure fluctuations measured on NACA 0008 and NACA 63018 airfoils at multiple angles of attack and inflow velocities, covering turbulent boundary layers with both adverse and favorable pressure gradients. Validation against experimental data (outside the training dataset) demonstrates the robustness of the model compared to well-accepted semi-empirical models. Finally, the model is integrated with Amiet's theory to predict the aeroacoustic noise of a full-scale wind turbine, showing good agreement with experimental measurements.

Related papers

• Integrating Physics and Data-Driven Approaches: An Explainable and Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction [1.1270209626877075]
  The rapid growth of the wind energy sector underscores the urgent need to optimize turbine operations. Traditional empirical and physics-based models offer approximate predictions of power generation based on wind speed. Data-driven machine learning methods present a promising avenue for improving wind turbine modeling.
  arXiv  Detail & Related papers  (2025-02-11T08:16:48Z)
• Robust Representation Consistency Model via Contrastive Denoising [83.47584074390842]
  randomized smoothing provides theoretical guarantees for certifying robustness against adversarial perturbations. diffusion models have been successfully employed for randomized smoothing to purify noise-perturbed samples. We reformulate the generative modeling task along the diffusion trajectories in pixel space as a discriminative task in the latent space.
  arXiv  Detail & Related papers  (2025-01-22T18:52:06Z)
• Efficient modeling of sub-kilometer surface wind with Gaussian processes and neural networks [0.0]
  Wind represents a particularly challenging variable to model due to its high spatial and temporal variability. This paper presents a novel approach that integrates Gaussian processes and neural networks to model surface wind gusts at sub-kilometer resolution.
  arXiv  Detail & Related papers  (2024-05-21T09:07:47Z)
• Robust Estimation of Causal Heteroscedastic Noise Models [7.568978862189266]
  Student's $t$-distribution is known for its robustness in accounting for sampling variability with smaller sample sizes and extreme values without significantly altering the overall distribution shape. Our empirical evaluations demonstrate that our estimators are more robust and achieve better overall performance across synthetic and real benchmarks.
  arXiv  Detail & Related papers  (2023-12-15T02:26:35Z)
• Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models [26.178192913986344]
  We make a first attempt to use denoising diffusion probabilistic models (DDPMs) to train an uncertainty-aware surrogate model for turbulence simulations. Our results show DDPMs can successfully capture the whole distribution of solutions and, as a consequence, accurately estimate the uncertainty of the simulations. We also evaluate an emerging generative modeling variant, flow matching, in comparison to regular diffusion models.
  arXiv  Detail & Related papers  (2023-12-08T19:04:17Z)
• A PAC-Bayesian Perspective on the Interpolating Information Criterion [54.548058449535155]
  We show how a PAC-Bayes bound is obtained for a general class of models, characterizing factors which influence performance in the interpolating regime. We quantify how the test error for overparameterized models achieving effectively zero training error depends on the quality of the implicit regularization imposed by e.g. the combination of model, parameter-initialization scheme.
  arXiv  Detail & Related papers  (2023-11-13T01:48:08Z)
• Understanding Pathologies of Deep Heteroskedastic Regression [25.509884677111344]
  Heteroskedastic models predict both mean and residual noise for each data point. At one extreme, these models fit all training data perfectly, eliminating residual noise entirely. At the other, they overfit the residual noise while predicting a constant, uninformative mean. We observe a lack of middle ground, suggesting a phase transition dependent on model regularization strength.
  arXiv  Detail & Related papers  (2023-06-29T06:31:27Z)
• Modeling the space-time correlation of pulsed twin beams [68.8204255655161]
  Entangled twin-beams generated by parametric down-conversion are among the favorite sources for imaging-oriented applications. We propose a semi-analytic model which aims to bridge the gap between time-consuming numerical simulations and the unrealistic plane-wave pump theory.
  arXiv  Detail & Related papers  (2023-01-18T11:29:49Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Leveraging Global Parameters for Flow-based Neural Posterior Estimation [90.21090932619695]
  Inferring the parameters of a model based on experimental observations is central to the scientific method. A particularly challenging setting is when the model is strongly indeterminate, i.e., when distinct sets of parameters yield identical observations. We present a method for cracking such indeterminacy by exploiting additional information conveyed by an auxiliary set of observations sharing global parameters.
  arXiv  Detail & Related papers  (2021-02-12T12:23:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.