Search
Now showing items 1-7 of 7
Modeling Low‐Flow Mixing through Pools and Riffles
Publisher: American Society of Civil Engineers
Abstract: The complex nature of low‐flow mixing in natural channels has been investigated using both laboratory experiments and the numerical solution of a proposed mathematical model that is based on a set of mass balance equations ...
Numerical Modeling of Normal Turbulent Plane Jet Impingement on Solid Wall
Publisher: American Society of Civil Engineers
Abstract: Jet impingement on a solid wall is a common problem for engineers. Near solid boundaries, flow situations depend highly on the impingement geometries. Therefore, it is almost impossible for engineers to labor through ...
Turbulent Plane Jet Structure
Publisher: American Society of Civil Engineers
Abstract: In the numerical modeling, solutions are highly subject to the boundary conditions that should mathematically represent as much physical phenomena as possible. However, in the predictions of a turbulent flow associated ...
Stall Inception in a Boundary Layer Ingesting Fan
Publisher: American Society of Mechanical Engineers (ASME)
Abstract: Jet engines with boundary layer ingestion (BLI) could offer significant reductions in aircraft fuel burn compared with podded turbofans. However, the engine fans must run continuously with severe inlet distortion, which ...
Mid-Span Stall Inception in Low-Pressure-Ratio Transonic Fans
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: In this paper, steady and unsteady computational fluid dynamics (CFD) have been used to investigate stall inception for a modern low-pressure-ratio transonic fan. The computational results are validated against measurement ...
Stall Inception in Low-Pressure Ratio Fans
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: A combined experimental and computational test program, with two low-pressure ratio aero-engine fans, has been used to identify the flow mechanisms at stall inception and the subsequent stall cell growth. The two fans have ...
Predicting the Operability of Damaged Compressors Using Machine Learning
Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: The application of machine learning to aerospace problems faces a particular challenge. For successful learning, a large amount of good quality training data is required, typically tens of thousands of cases. However, due ...