Sensitivities of Aircraft Acoustic Metrics to Engine Design Variables for Multidisciplinary Optimization

Abstract

Aircraft environmental performance metrics including fuel burn, takeoff and landing noise, and gaseous emissions are increasingly driving the design and optimization of modern aircraft engines. Gradient-based methods can significantly reduce the computational resources required during their multidisciplinary optimizations. However, current state-of-the art noise modeling tools do not allow for effective computation of sensitivities of acoustic metrics with respect to engine design and control variables, and therefore cannot be easily integrated into multidisciplinary optimization tools. This paper presents the utility of the python Noise Assessment tool (known as pyNA): an aircraft noise estimation model providing sensitivities of acoustic metrics, enabling multidisciplinary optimization and optimal control of engines for low-noise aircraft. The model is used to assess the trades of environmental performance metrics in the clean-sheet engine design for the NASA supersonic technology concept airplane. For the engine examined, if the fan diameter is allowed to change while technology levels are held constant, the takeoff noise levels and emissions are found to be most sensitive to the design fan pressure ratio. For an engine with a fixed fan diameter, takeoff noise levels are found to be insensitive to design variables; whereas nitrogen oxide emissions and cruise thrust-specific fuel consumption are most sensitive to the engine fan and high-pressure compressor pressure ratios.