Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow

• URL: http://arxiv.org/abs/2210.16248v1
• Date: Fri, 28 Oct 2022 16:27:14 GMT
• Title: Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow
• Authors: Mathis Bode
• Abstract summary: This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for LES subfilter modeling. It shows a successful application to a non-premixed temporal jet case.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper extends the methodology to use physics-informed enhanced super-resolution generative adversarial networks (PIESRGANs) for LES subfilter modeling in turbulent flows with finite-rate chemistry and shows a successful application to a non-premixed temporal jet case. This is an important topic considering the need for more efficient and carbon-neutral energy devices to fight the climate change. Multiple a priori and a posteriori results are presented and discussed. As part of this, the impact of the underlying mesh on the prediction quality is emphasized, and a multi-mesh approach is developed. It is demonstrated how LES based on PIESRGAN can be employed to predict cases at Reynolds numbers which were not used for training. Finally, the amount of data needed for a successful prediction is elaborated.

Related papers

• Diffusion Models as Network Optimizers: Explorations and Analysis [71.69869025878856]
  generative diffusion models (GDMs) have emerged as a promising new approach to network optimization. In this study, we first explore the intrinsic characteristics of generative models. We provide a concise theoretical and intuitive demonstration of the advantages of generative models over discriminative network optimization.
  arXiv  Detail & Related papers  (2024-11-01T09:05:47Z)
• Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation [49.49868273653921]
  Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving. We introduce Optimal Gaussian Diffusion (OGD) and Estimated Clean Manifold (ECM) Guidance. Our methodology streamlines the generative process, enabling practical applications with reduced computational overhead.
  arXiv  Detail & Related papers  (2024-08-01T17:59:59Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics [0.0]
  This paper demonstrates the development and application of a deep learning framework for predictions of the flow field and aerodynamic performance of multi-stage axial compressors. A physics-based dimensionality reduction unlocks the potential for flow-field predictions. The proposed architecture is proven to achieve an accuracy comparable to that of the CFD benchmark, in real-time, for an industrially relevant application.
  arXiv  Detail & Related papers  (2023-10-06T14:11:21Z)
• Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
  It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning. Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
  arXiv  Detail & Related papers  (2023-07-25T14:09:53Z)
• Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Finite-Rate-Chemistry Flows and Predicting Lean Premixed Gas Turbine Combustors [0.0]
  This work advances the recently introduced PIESRGAN to reactive finite-rate-chemistry flows. The modified PIESRGAN-based model gives good agreement in a priori and a posteriori tests in a laminar lean premixed combustion setup.
  arXiv  Detail & Related papers  (2022-10-28T15:48:26Z)
• Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data [0.0]
  This work advances the recently developed PIESRGAN modeling approach to turbulent premixed combustion. The resulting model provides good results for a priori and a posteriori tests on direct numerical simulation data of a fully turbulent premixed flame kernel.
  arXiv  Detail & Related papers  (2022-10-28T15:27:46Z)
• How Tempering Fixes Data Augmentation in Bayesian Neural Networks [22.188535244056016]
  We show that tempering implicitly reduces the misspecification arising from modeling augmentations as i.i.d. data. The temperature mimics the role of the effective sample size, reflecting the gain in information provided by the augmentations.
  arXiv  Detail & Related papers  (2022-05-27T11:06:56Z)
• Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future [73.03458424369657]
  In real-time forecasting in public health, data collection is a non-trivial and demanding task. 'Backfill' phenomenon and its effect on model performance has been barely studied in the prior literature. We formulate a novel problem and neural framework Back2Future that aims to refine a given model's predictions in real-time.
  arXiv  Detail & Related papers  (2021-06-08T14:48:20Z)
• Probabilistic electric load forecasting through Bayesian Mixture Density Networks [70.50488907591463]
  Probabilistic load forecasting (PLF) is a key component in the extended tool-chain required for efficient management of smart energy grids. We propose a novel PLF approach, framed on Bayesian Mixture Density Networks. To achieve reliable and computationally scalable estimators of the posterior distributions, both Mean Field variational inference and deep ensembles are integrated.
  arXiv  Detail & Related papers  (2020-12-23T16:21:34Z)
• DCTRGAN: Improving the Precision of Generative Models with Reweighting [1.2622634782102324]
  We introduce a post-hoc correction to deep generative models to further improve their fidelity. The correction takes the form of a reweighting function that can be applied to generated examples. We show that the weighted GAN examples significantly improve the accuracy of the generated samples without a large loss in statistical power.
  arXiv  Detail & Related papers  (2020-09-03T18:00:27Z)

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.