Electrified aircraft propulsion enables new aircraft designs with fewer emissions. One challenge of electrified architectures is handling the electrical components’ waste heat. This is because batteries and other electrical components are sensitive to high temperatures and accumulate heat within their structure. In this work, we investigate using a thermoacoustic refrigerator to cool the battery of a parallel hybrid single-aisle commercial transport aircraft. This thermoacoustic refrigeration system is powered by waste heat from the turbofan engine core, whereas a conventional refrigerator consumes electricity from the battery or shaft power offtakes. Compared to a conventional vapor cycle refrigerator, the thermoacoustic refrigeration system results in greater mission fuel burn because of pressure losses attributable to the extraction of heat from the turbofan to drive the thermoacoustic refrigerator. Heat exchangers with very low pressure losses may render the thermoacoustic refrigeration system beneficial compared to conventional refrigeration in certain use cases, such as low-altitude missions.

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Aerostructural optimization traditionally uses a single or small number of cruise conditions to estimate the mission fuel burn objective function. In reality, a mission includes other flight segments contributing to fuel burn, such as climbing and descent. We aim to quantify how much performance is sacrificed by optimizing the design for a fuel burn approximation that ignores these other flight segments and flight conditions. To do this, we compare traditional approaches to mission-based optimization, which uses an accurate fuel burn objective computed by numerically integrating fuel flow across the mission profile. We find that mission-based optimization offers only marginal benefits over traditional single-point and multipoint approaches for aerostructural optimization of a narrow-body aircraft—only 1–2% in the most extreme cases. Thus, the traditional aerostructural optimization is acceptable, especially in cases where most fuel is burned during cruise. For the cases where climb fuel burn is significant, we introduce a simple change to traditional fuel burn approximation methods that allows the optimizer to find nearly all the fuel burn reduction of mission-based optimization but at the computational cost of multipoint optimization.