In a fiscally constrained environment, it is crucial that both equipment manufacturers and defence invest in technology that shows marked operational improvement. A priori identification of cost-benefit at the early acquisition stage is often limited and incomplete, leading to poor value propositions. This conundrum motivates the need to develop a method to evaluate technologies such as levels of autonomy, stealth capability, improved engines, etc. and make tradeoffs against operational measures of performance and effectiveness (MOP/Es) rather than solely against vehicle performance characteristics. The objective of this study is to create an environment in which those trades against MOEs could be performed rapidly to inform technology investment and acquisition decision-making. This environment is built on top of representative models of a discrete event simulation of disaster relief airlift operations to compare technology modifications or vehicle acquisition options rapidly against operational measures of effectiveness.
With the increasing research efforts in civil supersonic transport (SST) during the past decade, companies like Boom and Aerion are making the comeback of civil supersonic flight more promising than ever. Both companies believe that substantial demand exists in civil supersonic aviation, and opportunities are present. However, many regulatory hurdles and operational constraints impose strict limitations on supersonic flight and should not be overlooked. In addition, these aircraft are likely to have higher fuel burn per passenger compared to that for similarly-sized subsonic aircraft, and their effect on fleet-level emissions is unknown. In Part I of this two-part study, the research team successfully demonstrated a methodology that employs a bottom-up approach for estimating the future demand for supersonic commercial operation and its associated fuel burn and CO 2 emission, using only publicly available subsonic baseline-fleet data. This paper seeks to fill the gaps and assumptions identified in the Part I paper by using robust, non-public data, and provides updated results on market estimation and environmental impact (in terms of both CO 2 and NO x ) between 2035 and 2050.
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