UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration

• URL: http://arxiv.org/abs/2411.16693v1
• Date: Sat, 09 Nov 2024 20:56:05 GMT
• Title: UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration
• Authors: Georgios Georgalis, Alejandro Becerra, Kenneth Budzinski, Matthew McGurn, Danial Faghihi, Paul E. DesJardin, Abani Patra,
• Abstract summary: This paper demonstrates and address challenges related to performing a complete uncertainty quantification (UQ) analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS) Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. Analysis revealed that the surrogates do not accurately predict all spatial locations of the slab burner as-is.
• Score: 36.136619420474766
• License:
• Abstract: The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification (UQ) analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertainties to the fuel regression rate--the primary quantity of interest--and Bayesian calibration of critical parameters influencing the regression rate using experimental data. Specifically, the parameters calibrated include the latent heat of sublimation and a chemical reaction temperature exponent. Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. Both models exhibited comparable performance during cross-validation. However, the HMS was more stable due to its ability to handle multiscale effects, in contrast with the GP which was very sensitive to kernel choice. Analysis revealed that the surrogates do not accurately predict all spatial locations of the slab burner as-is. Subsequent Bayesian calibration of the physical parameters against experimental observations resulted in regression rate predictions that closer align with experimental observation in specific regions. This study highlights the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems.

Related papers

• FUSE: Fast Unified Simulation and Estimation for PDEs [11.991297011923004]
  We argue that solving both problems within the same framework can lead to consistent gains in accuracy and robustness. We present the capabilities of the proposed methodology for predicting continuous and discrete biomarkers in full-body haemodynamics simulations.
  arXiv  Detail & Related papers  (2024-05-23T13:37:26Z)
• Conditional Korhunen-Lo\'{e}ve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling [62.997667081978825]
  We propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems. We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response.
  arXiv  Detail & Related papers  (2023-07-05T18:14:38Z)
• Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts [40.24720443257405]
  We provide a convergence analysis for maximum likelihood estimation (MLE) in the Gaussian-gated MoE model. Our findings reveal that the MLE has distinct behaviors under two complement settings of location parameters of the Gaussian gating functions. Notably, these behaviors can be characterized by the solvability of two different systems of equations.
  arXiv  Detail & Related papers  (2023-05-12T16:02:19Z)
• Optimised Bayesian system identification in quantum devices [3.72081359624651]
  We present a closed-loop Bayesian learning algorithm for estimating unknown parameters in a dynamical model. We demonstrate the performance of the algorithm in both simulated calibration tasks and in an experimental single-qubit ion-trap system.
  arXiv  Detail & Related papers  (2022-11-16T18:12:46Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Model Calibration of the Liquid Mercury Spallation Target using Evolutionary Neural Networks and Sparse Polynomial Expansions [4.3634848427203945]
  We present two approaches for surrogate-based model calibration of expensive simulations using evolutionary neural networks and sparse expansions. The proposed simulations can significantly aid in fatigue analysis to estimate the mercury target lifetime and integrity. However, an important conclusion from this work points out to a deficiency in the current model based on the equation of state in capturing the full physics of the spallation reaction.
  arXiv  Detail & Related papers  (2022-02-18T18:47:10Z)
• Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning [56.67751936864119]
  The present work aims to construct cheap-to-compute machine learning (ML) models to act as closure equations for predicting the physical properties of alternative fuels. Those models can be trained using the database from MD simulations and/or experimental measurements in a data-fusion-fidelity approach. The results show that ML models can predict accurately the fuel properties of a wide range of pressure and temperature conditions.
  arXiv  Detail & Related papers  (2021-10-18T14:43:50Z)
• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration [57.568461777747515]
  We introduce a novel calibration method, Parametrized Temperature Scaling (PTS) We demonstrate that the performance of accuracy-preserving state-of-the-art post-hoc calibrators is limited by their intrinsic expressive power. We show with extensive experiments that our novel accuracy-preserving approach consistently outperforms existing algorithms across a large number of model architectures, datasets and metrics.
  arXiv  Detail & Related papers  (2021-02-24T10:18:30Z)
• 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)
• Validity of Born-Markov master equations for single and two-qubit systems [0.0]
  We compare the commonly used Redfield and Lindblad master equations against numerically exact results. Our findings set general guidelines for the range of validity of the usual Born-Markov master equations.
  arXiv  Detail & Related papers  (2020-11-10T11:19:00Z)

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.