[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
Dimensional decomposition-aided metamodels for uncertainty quantification and optimization in engineering: A review
Quantitative analysis and optimal design under uncertainty are active research areas in
modern engineering structures and systems. A metamodel, as an effective mathematical …
modern engineering structures and systems. A metamodel, as an effective mathematical …
Reduced-order modeling of supersonic fuel–air mixing in a multi-strut injection scramjet engine using machine learning techniques
Dual-mode ramjet/scramjet engines promise extended flight speed range and are the
commonly preferred air-breathing propulsion system from within the family of hypersonic …
commonly preferred air-breathing propulsion system from within the family of hypersonic …
A general framework for building surrogate models for uncertainty quantification in computational electromagnetics
R Hu, V Monebhurrun, R Himeno… - … on Antennas and …, 2021 - ieeexplore.ieee.org
In uncertainty analysis, surrogate modeling techniques demonstrate high efficiency and
reliable precision in estimating the uncertainty for the finite difference time domain (FDTD) …
reliable precision in estimating the uncertainty for the finite difference time domain (FDTD) …
Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system
JP Spinti, PJ Smith, ST Smith, OH Díaz-Ibarra - Applied Energy, 2023 - Elsevier
We describe the integration of Bayesian decision theory in a digital twin framework to
provide a process optimization tool for a biomass boiler. Our application is the Atikokan …
provide a process optimization tool for a biomass boiler. Our application is the Atikokan …
[HTML][HTML] Model order reduction by proper orthogonal decomposition for a 500 MWe tangentially fired pulverized coal boiler
Reduced order models (ROMs) are constructed by proper orthogonal decomposition (POD)
and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe …
and regression by Kriging and Radial Basis Neural Network (RBFN) for a 500 MWe …
Uncertainty analysis for hydrological models with interdependent parameters: an improved polynomial chaos expansion approach
The use of polynomial chaos expansion (PCE) has gained a lot of attention due to its ability
to efficiently estimate the effects of parameter uncertainty on model outputs. The traditional …
to efficiently estimate the effects of parameter uncertainty on model outputs. The traditional …
[图书][B] A surrogate-assisted Bayesian framework for uncertainty-aware validation benchmarks
F Mohammadi - 2023 - elib.uni-stuttgart.de
Over the last century, computational modeling in geoscience, especially in porous media
research, has witnessed tremendous improvement. After decades of development, the state …
research, has witnessed tremendous improvement. After decades of development, the state …
Parameter Estimation Using a Gaussian Process Regression-Based Reduced-Order Model and Sparse Sensing: Application to a Methane/Air Lifted Jet Flame
The goal of this work is to perform parameter estimation by comparing a Reduced Order
Model (ROM), built using Proper Orthogonal Decomposition (POD) and Gaussian Process …
Model (ROM), built using Proper Orthogonal Decomposition (POD) and Gaussian Process …
Surrogate-assisted modeling and robust optimization of a micro gas turbine plant with carbon capture
S Giorgetti, D Coppitters… - … for Gas Turbines …, 2020 - asmedigitalcollection.asme.org
The growing share of wind and solar power in the total energy mix has caused severe
problems in balancing the electrical power production. Consequently, in the future, all fossil …
problems in balancing the electrical power production. Consequently, in the future, all fossil …