The Experts below are selected from a list of 198501 Experts worldwide ranked by ideXlab platform
Robert Youngblood - One of the best experts on this subject based on the ideXlab platform.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation
Nuclear Engineering and Design, 2019Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Abstract Over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire Framework.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation.
arXiv: Computational Physics, 2018Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal transient and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of coarse mesh size and models for low-fidelity system-level thermal-hydraulic simulation, such as coarse-mesh Computational Fluid Dynamics-like (CFD-like) codes, to achieve accuracy comparable to that of high-fidelity simulation, such as high-resolution CFD. Based on high-fidelity data and massive fast-running low-fidelity simulations, error database is built and used to train a machine learning model and find the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with methods and algorithms in the state of the art. A mixed convection case study was performed to illustrate the entire Framework.
Han Bao - One of the best experts on this subject based on the ideXlab platform.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation
Nuclear Engineering and Design, 2019Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Abstract Over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire Framework.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation.
arXiv: Computational Physics, 2018Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal transient and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of coarse mesh size and models for low-fidelity system-level thermal-hydraulic simulation, such as coarse-mesh Computational Fluid Dynamics-like (CFD-like) codes, to achieve accuracy comparable to that of high-fidelity simulation, such as high-resolution CFD. Based on high-fidelity data and massive fast-running low-fidelity simulations, error database is built and used to train a machine learning model and find the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with methods and algorithms in the state of the art. A mixed convection case study was performed to illustrate the entire Framework.
Nam Dinh - One of the best experts on this subject based on the ideXlab platform.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation
Nuclear Engineering and Design, 2019Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Abstract Over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire Framework.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation.
arXiv: Computational Physics, 2018Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal transient and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of coarse mesh size and models for low-fidelity system-level thermal-hydraulic simulation, such as coarse-mesh Computational Fluid Dynamics-like (CFD-like) codes, to achieve accuracy comparable to that of high-fidelity simulation, such as high-resolution CFD. Based on high-fidelity data and massive fast-running low-fidelity simulations, error database is built and used to train a machine learning model and find the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with methods and algorithms in the state of the art. A mixed convection case study was performed to illustrate the entire Framework.
Jeffrey W. Lane - One of the best experts on this subject based on the ideXlab platform.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation
Nuclear Engineering and Design, 2019Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Abstract Over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire Framework.
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A Data-Driven Framework for Error Estimation and Mesh-Model Optimization in System-level Thermal-Hydraulic Simulation.
arXiv: Computational Physics, 2018Co-Authors: Han Bao, Nam Dinh, Jeffrey W. Lane, Robert YoungbloodAbstract:Over the past decades, several computer codes were developed for simulation and analysis of thermal-hydraulics of system behaviors in nuclear reactors under operating, abnormal transient and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-Driven Framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of coarse mesh size and models for low-fidelity system-level thermal-hydraulic simulation, such as coarse-mesh Computational Fluid Dynamics-like (CFD-like) codes, to achieve accuracy comparable to that of high-fidelity simulation, such as high-resolution CFD. Based on high-fidelity data and massive fast-running low-fidelity simulations, error database is built and used to train a machine learning model and find the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS Framework is designed as a modularized six-step procedure and accomplished with methods and algorithms in the state of the art. A mixed convection case study was performed to illustrate the entire Framework.
Kewen Wang - One of the best experts on this subject based on the ideXlab platform.
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understanding the influence of configuration settings an execution model Driven Framework for apache spark platform
International Conference on Cloud Computing, 2017Co-Authors: Nhan Nguyen, Mohammad Maifi Hasan Khan, Yusuf Albayram, Kewen WangAbstract:Apache Spark provides numerous configuration settings that can be tuned to improve the performance of specific applications running on the platform. However, due to its multi-stage execution model and high interactive complexity across nodes, it is nontrivial to understand how/why a specific setting influences the execution flow and performance. To address this challenge, we develop an execution model-Driven Framework that extracts key performance metrics relevant to different levels of execution (e.g., application level, stage level, task level, system level) and applies statistical analysis techniques to identify the key execution features that change significantly in response to changes in configuration settings. This allows users to answer questions such as "How does configuration setting X affect the execution behavior of Spark?" or "Why does changing configuration setting X degrade the performance of Spark application Y?". We tested our Framework using 6 open source applications (e.g., Word Count, Tera Sort, KMeans, Matrix Factorization, PageRank, and Triangle Count) and demonstrated the effectiveness of our Framework in identifying the underlying reasons behind changes in performance.
