Hybrid RANS-LES methods offer a means of reducing computational cost and setup time to simulate transitional flows. Several methods are evaluated in ANSYS CFX, including Scale-Adaptive Simulation (SAS), Shielded Detac...Hybrid RANS-LES methods offer a means of reducing computational cost and setup time to simulate transitional flows. Several methods are evaluated in ANSYS CFX, including Scale-Adaptive Simulation (SAS), Shielded Detached Eddy Simulation (SDES), Stress-Blended Eddy Simulation (SBES), and Zonal Large Eddy Simulation (ZLES), along with a no-model laminar simulation. Each is used to simulate an adiabatic flat plate film cooling experiment of a shaped hole at low Reynolds number. Adiabatic effectiveness is calculated for Blowing Ratio (BR) = 1.5 and Density Ratio (DR) = 1.5. The ZLES method and laminar simulation most accurately match experimental lateral-average adiabatic effectiveness along the streamwise direction from the trailing edge of the hole to 35 hole diameters downstream of the hole (X/D = 0 to X/D = 35), with RMS deviations of 5.1% and 4.2%, and maximum deviations of 8% and 11%, respectively. The accuracy of these models is attributed to the resolution of turbulent structures in not only the mixing region but in the upstream boundary layer as well, where the other methods utilize RANS and do not switch to LES.展开更多
This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem （MS-SHM-Hadoop）. By taking the advantages ...This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem （MS-SHM-Hadoop）. By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System （HDFS） and high-performance parallel data processing engine called MapReduce programming paradigm, MS- SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge service- ability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method （e.g., association rule learning） or structural mechanics modeling and simulation.展开更多
文摘Hybrid RANS-LES methods offer a means of reducing computational cost and setup time to simulate transitional flows. Several methods are evaluated in ANSYS CFX, including Scale-Adaptive Simulation (SAS), Shielded Detached Eddy Simulation (SDES), Stress-Blended Eddy Simulation (SBES), and Zonal Large Eddy Simulation (ZLES), along with a no-model laminar simulation. Each is used to simulate an adiabatic flat plate film cooling experiment of a shaped hole at low Reynolds number. Adiabatic effectiveness is calculated for Blowing Ratio (BR) = 1.5 and Density Ratio (DR) = 1.5. The ZLES method and laminar simulation most accurately match experimental lateral-average adiabatic effectiveness along the streamwise direction from the trailing edge of the hole to 35 hole diameters downstream of the hole (X/D = 0 to X/D = 35), with RMS deviations of 5.1% and 4.2%, and maximum deviations of 8% and 11%, respectively. The accuracy of these models is attributed to the resolution of turbulent structures in not only the mixing region but in the upstream boundary layer as well, where the other methods utilize RANS and do not switch to LES.
文摘This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem （MS-SHM-Hadoop）. By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System （HDFS） and high-performance parallel data processing engine called MapReduce programming paradigm, MS- SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge service- ability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method （e.g., association rule learning） or structural mechanics modeling and simulation.
