A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface

• URL: http://arxiv.org/abs/2402.10641v1
• Date: Fri, 16 Feb 2024 12:41:31 GMT
• Title: A Predictive Surrogate Model for Heat Transfer of an Impinging Jet on a Concave Surface
• Authors: Sajad Salavatidezfouli, Saeid Rakhsha, Armin Sheidani, Giovanni Stabile and Gianluigi Rozza
• Abstract summary: This paper aims to investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. To this end, this work introduces two predictive approaches, one employing a Fast Fourier Transformation augmented Artificial Neural Network (FFT-ANN) for predicting the average Nusselt number under constant-frequency scenarios. The POD-LSTM method proves to be a robust solution for predicting the local heat transfer rate under random-frequency impingement scenarios, capturing both the trend and value of temporal modes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper aims to comprehensively investigate the efficacy of various Model Order Reduction (MOR) and deep learning techniques in predicting heat transfer in a pulsed jet impinging on a concave surface. Expanding on the previous experimental and numerical research involving pulsed circular jets, this investigation extends to evaluate Predictive Surrogate Models (PSM) for heat transfer across various jet characteristics. To this end, this work introduces two predictive approaches, one employing a Fast Fourier Transformation augmented Artificial Neural Network (FFT-ANN) for predicting the average Nusselt number under constant-frequency scenarios. Moreover, the investigation introduces the Proper Orthogonal Decomposition and Long Short-Term Memory (POD-LSTM) approach for random-frequency impingement jets. The POD-LSTM method proves to be a robust solution for predicting the local heat transfer rate under random-frequency impingement scenarios, capturing both the trend and value of temporal modes. The comparison of these approaches highlights the versatility and efficacy of advanced machine learning techniques in modelling complex heat transfer phenomena.

Related papers

• Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN [3.2686289567336235]
  In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Recent advancements highlight the efficacy of diffusion models as state-of-the-art generative machine learning methods. We present the first simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on diffusion models, achieving the highest fidelity compared to existing baselines.
  arXiv  Detail & Related papers  (2024-06-05T13:11:53Z)
• EM Distillation for One-step Diffusion Models [65.57766773137068]
  We propose a maximum likelihood-based approach that distills a diffusion model to a one-step generator model with minimal loss of quality. We develop a reparametrized sampling scheme and a noise cancellation technique that together stabilizes the distillation process.
  arXiv  Detail & Related papers  (2024-05-27T05:55:22Z)
• 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 (GPs) and neural networks to model surface wind gusts. We discuss the effect of different modeling choices, as well as different degrees of approximation, and present our results for a case study.
  arXiv  Detail & Related papers  (2024-05-21T09:07:47Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets [0.0]
  We present a novel deep probabilistic learning approach, the 'Stochastic Latent Transformer' (SLT) The SLT accurately reproduces system dynamics across various integration periods, validated through quantitative diagnostics. It achieves a five-order-of-magnitude speedup in emulating the zonally-averaged flow.
  arXiv  Detail & Related papers  (2023-10-25T16:17:00Z)
• DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting [18.86526240105348]
  We propose an approach for efficiently training diffusion models for probabilistic forecasting. We train a time-conditioned interpolator and a forecaster network that mimic the forward and reverse processes of standard diffusion models. Our approach performs competitively on probabilistic forecasting of complex dynamics in sea surface temperatures, Navier-Stokes flows, and flows spring systems.
  arXiv  Detail & Related papers  (2023-06-03T02:46:31Z)
• ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories [144.03939123870416]
  We propose a novel conditional diffusion model by introducing conditions into the forward process. We use extra latent space to allocate an exclusive diffusion trajectory for each condition based on some shifting rules. We formulate our method, which we call textbfShiftDDPMs, and provide a unified point of view on existing related methods.
  arXiv  Detail & Related papers  (2023-02-05T12:48:21Z)
• 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)
• How Much is Enough? A Study on Diffusion Times in Score-based Generative Models [76.76860707897413]
  Current best practice advocates for a large T to ensure that the forward dynamics brings the diffusion sufficiently close to a known and simple noise distribution. We show how an auxiliary model can be used to bridge the gap between the ideal and the simulated forward dynamics, followed by a standard reverse diffusion process.
  arXiv  Detail & Related papers  (2022-06-10T15:09:46Z)
• Photoinduced prethermal order parameter dynamics in the two-dimensional large-$N$ Hubbard-Heisenberg model [77.34726150561087]
  We study the microscopic dynamics of competing ordered phases in a two-dimensional correlated electron model. We simulate the light-induced transition between two competing phases.
  arXiv  Detail & Related papers  (2022-05-13T13:13:31Z)
• A latent variable approach to heat load prediction in thermal grids [10.973034520723957]
  The method is applied to a single multi-dwelling building in Lulea, Sweden. Results are compared with predictions using an artificial neural network.
  arXiv  Detail & Related papers  (2020-02-13T09:21:17Z)

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.