Data-driven temperature estimation of non-contact solids using deep-learning reduced-order models

2022 ◽  
Vol 185 ◽  
pp. 122383
Author(s):  
Genghui Jiang ◽  
Ming Kang ◽  
Zhenwei Cai ◽  
Yingzheng Liu ◽  
Weizhe Wang
Keyword(s):  
Deep Learning ◽  
Data Driven ◽  
Reduced Order Models ◽  
Temperature Estimation ◽  
Reduced Order

scholarly journals Data-driven variational multiscale reduced order models

2021 ◽  
Vol 373 ◽  
pp. 113470 ◽  
Author(s):  
Changhong Mou ◽  
Birgul Koc ◽  
Omer San ◽  
Leo G. Rebholz ◽  
Traian Iliescu
Keyword(s):  
Data Driven ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Variational Multiscale

scholarly journals Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures

2018 ◽  
Vol 154 ◽  
pp. 170-183 ◽  
Author(s):  
Noah H. Paulson ◽  
Matthew W. Priddy ◽  
David L. McDowell ◽  
Surya R. Kalidindi
Keyword(s):  
High Cycle Fatigue ◽  
Fatigue Performance ◽  
Data Driven ◽  
Reduced Order Models ◽  
Cycle Fatigue ◽  
Reduced Order ◽  
Rank Ordering

Uncertainty quantification of bladed disc systems using data driven stochastic reduced order models

2021 ◽  
Vol 190 ◽  
pp. 106011
Author(s):  
Rahul Kumar ◽  
Shaikh Faruque Ali ◽  
Sankarkumar Jeyaraman ◽  
Sayan Gupta
Keyword(s):  
Uncertainty Quantification ◽  
Data Driven ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Using Data

Physics-Aware Deep-Learning-Based Proxy Reservoir Simulation Model Equipped with State and Well Output Prediction

2021 ◽  
Author(s):  
Emilio J. R. Coutinho ◽  
Marcelo J. Aqua and Eduardo Gildin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reservoir Simulation ◽  
Data Driven ◽  
Training Procedure ◽  
State Variables ◽  
Input Output ◽  
Well Control ◽  
Reduced Order

Abstract Physics-aware machine learning (ML) techniques have been used to endow data-driven proxy models with features closely related to the ones encountered in nature. Examples span from material balance and conservation laws. Physics-based and data-driven reduced-order models or a combination thereof (hybrid-based models) can lead to fast, reliable, and interpretable simulations used in many reservoir management workflows. We built on a recently developed deep-learning-based reduced-order modeling framework by adding a new step related to information of the input-output behavior (e.g., well rates) of the reservoir and not only the states (e.g., pressure and saturation) matching. A Combination of data-driven model reduction strategies and machine learning (deep- neural networks – NN) will be used here to achieve state and input-output matching simultaneously. In Jin, Liu and Durlofsky (2020), the authors use a NN architecture where it is possible to predict the state variables evolution after training an autoencoder coupled with a control system approach (Embed to Control - E2C) and adding some physical components (Loss functions) to the neural network training procedure. In this paper, we extend this idea by adding the simulation model output, e.g., well bottom-hole pressure and well flowrates, as data to be used in the training procedure. Additionally, we added a new neural network to the E2C transition model to handle the connections between state variables and model outputs. By doing this, it is possible to estimate the evolution in time of both the state variables as well as the output variables simultaneously. The method proposed provides a fast and reliable proxy for the simulation output, which can be applied to a well-control optimization problem. Such a non-intrusive method, like data-driven models, does not need to have access to reservoir simulation internal structure. So it can be easily applied to commercial reservoir simulations. We view this as an analogous step to system identification whereby mappings related to state dynamics, inputs (controls), and measurements (output) are obtained. The proposed method is applied to an oil-water model with heterogeneous permeability, 4 injectors, and 5 producer wells. We used 300 sampled well control sets to train the autoencoder and another set to validate the obtained autoencoder parameters.

Hybrid Data-Driven Reduced Order Models for the 3D Turbulent Channel Flow

2021 ◽  
Author(s):  
Changhong Mou ◽  
Honghu Liu ◽  
Leo G. Rebholz ◽  
Traian Iliescu
Keyword(s):  
Channel Flow ◽  
Turbulent Channel Flow ◽  
Data Driven ◽  
Reduced Order Models ◽  
Reduced Order ◽  
Hybrid Data
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