PROJECT TITLE: High-Accuracy Solution-Adaptive Method for Turbomachinery Noise Prediction
TECHNICAL ABSTRACT (LIMIT 200 WORDS)
The proposed work involves a new unstructured macro-cell algorithm for computational aeroacoustics (CAA), which may be two to three orders of magnitude more efficient than most current methods. The unstructured macro-cell algorithm has features of both structured and unstructured grid methods; it retains the efficiency and accuracy of structured grid methods and much of the adaptability of unstructured grid methods. The governing equations are solved on a macro-cell, a structured grid consisting of roughly 15 grid points in each spatial direction, using high-accuracy, explicit or implicit, finite-volume methods. A full computational domain is composed of perhaps thousands of macro-cells, arranged in an unstructured manner. The elements contributing to high efficiency include high-accuracy spatial discretization, good time integration, and spatial and temporal solution adaptivity. An advanced treatment of the slip-interference between domains in relative motion will be based on macro-cell boundary methods. These technologies will be validated on suitable CAA test cases and applied to blade/wake interactions representative of fan noise generation. The accuracy and computational efficiency of the new unstructured macro-cell method will be demonstrated.
POTENTIAL COMMERCIAL APPLICATIONS
The proposed method will provide significant accuracy and efficiency advantages over existing methods used to predict noise generation in turbomachinery. These methods can be used in the design of advanced aircraft engines, and in other industries concerned with aeroacoustic noise generation.
NAME AND ADDRESS OF PRINCIPAL INVESTIGATOR
Robert E. Childs
Nielsen Engineering & Research, Inc.
526 Clyde Avenue
Mountain View , CA 94043-2212
NAME AND ADDRESS OF OFFEROR
Nielsen Engineering & Research, Inc.
526 Clyde Avenue
Mountain View , CA 94043-2212