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Xun Jiang

Publications and source records attributed to Xun Jiang.

4 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Estimations of Aircraft and Airport Domestic Greenhouse Gas Emissions from 2016-2021

Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.

Susie Go↗
NASA NTRS2021

Titan’s Global Radiant Energy Budget During the Cassini Epoch (2004-2017)

Radiant energies of planets and moons are of wide interest in the fields of geoscience and planetary science. Based on long-term multiinstrument observations from the Cassini spacecraft, we provide here the first observational study of Titan's global radiant energy budget and its seasonal variations. Our results show that Titan's radiant energy budget is not balanced over the Cassini era (2004–2017) with the absorbed solar energy (1.208 ± 0.008) × 10^(23) J larger than the emitted thermal energy (1.174 ± 0.005) × 10^(23) J. The energy imbalance is 2.9 ± 0.8% of the emitted thermal energy. Titan's global radiant energy budget is not balanced either at the timescales of Earth's years and Titan's seasons. In particular, the energy imbalance can be beyond 10% of the emitted thermal energy at the timescale of an Earth year. The energy imbalance revealed in this study has important impacts on Titan, which should be examined further by theories and models.

Ellen C. Creecy↗
NASA NTRSDate not supplied

Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft With Analytic Gradients

Demands on aircraft design methods in recent years have begun to require increasingly higher amounts of coupling between disciplines and have also begun to require optimization in order to satisfy competing objectives involving large numbers of parameters that define unconventional configurations. These expanding requirements have amplified a need for new and improved aircraft design, analysis, and optimization codes that are capable of performing coupled design exploiting analytic gradients where possible. Aviary is a multidisciplinary design optimization and analysis framework which allows for tightly coupled simultaneous aircraft and subsystem design using analytic gradients. Aviary has employed the methods of two legacy aircraft analysis tools to provide native analytically differentiated calculations for five different disciplines, and it also has the ability to couple in external discipline analysis tools, whether or not those tools can provide analytic gradients. Preliminary examples and modeling efforts have shown Aviary’s ability to effectively model novel concepts and explore large and non-intuitive design spaces. Finally, a multi-level user interface in Aviary creates an easy entry point for users with any level of multidisciplinary design, analysis, and optimization experience.

multidisciplinary↗