Influencing Inclusion and Innovation: Practical Tools to Develop Leaders
No abstract available
SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
No abstract available
A collection of abstracts from the 2020 Student Programs Cohort at Neil A. Armstrong Flight Research Center.
This research project sought to develop short-term and long-term projections on the outlook of air transportation and to produce relevant recommendations for the direction of NASA aviation research as it adapts to the disrupted industry. We developed a model to estimate airline recovery trajectories, and researched the unique effects of the pandemic on various sectors of aviation. We found that the pandemic highlighted past flaws in the aviation system, creating widespread effects across the industry. As the industry looks towards recovery, we believe that it cannot simply return to 2019 operations, but instead perform a full reinvention to support long-term demand and prepare for future catastrophes. We recommend that NASA seize this opportunity to accelerate innovation through an increased focus on passenger satisfaction, meaningful steps towards sustainability, and significant collaboration with a diverse range of groups.
This research project sought to develop short-term and long-term projections on the outlook of air transportation and to produce relevant recommendations for the direction of NASA aviation research as it adapts to the disrupted industry. We developed a model to estimate airline recovery trajectories, and researched the unique effects of the pandemic on various sectors of aviation. We found that the pandemic highlighted past flaws in the aviation system, creating widespread effects across the industry. As the industry looks towards recovery, we believe that it cannot simply return to 2019 operations, but instead perform a full reinvention to support long-term demand and prepare for future catastrophes. We recommend that NASA seize this opportunity to accelerate innovation through an increased focus on passenger satisfaction, meaningful steps towards sustainability, and significant collaboration with a diverse range of groups.
This research project sought to develop short-term and long-term projections on the outlook of air transportation and to produce relevant recommendations for the direction of NASA aviation research as it adapts to the disrupted industry. We developed a model to estimate airline recovery trajectories, and researched the unique effects of the pandemic on various sectors of aviation. We found that the pandemic highlighted past flaws in the aviation system, creating widespread effects across the industry. As the industry looks towards recovery, we believe that it cannot simply return to 2019 operations, but instead perform a full reinvention to support long-term demand and prepare for future catastrophes. We recommend that NASA seize this opportunity to accelerate innovation through an increased focus on passenger satisfaction, meaningful steps towards sustainability, and significant collaboration with a diverse range of groups.
Explore the source record for details and available documents.
The PIP-II linac will enable >1.2 MW beam power for DUNE, requiring unprecedented operational reliability across its warm front-end (RFQ, MEBT) and five distinct SRF sections operating at 162.5/325/650 MHz. We present a comprehensive digital twin framework uniquely combining a fully differentiable fast beam transport code with neural network surrogates trained on high-fidelity PIC simulations, capturing space charge and nonlinear dynamics beyond traditional envelope codes while achieving 10⁴× speedup at <1% accuracy. End-to-end differentiability enables gradient-based optimization across 500+ parameters simultaneously—previously impossible with conventional tools—while the model incorporates static/dynamic errors and serves as a virtual commissioning platform for diverse hardware integration. The framework facilitates reinforcement learning for pulsed/CW mode transitions, predictive maintenance through anomaly detection, and autonomous tuning algorithm development with real-time execution capability. Validation against physics simulations shows excellent agreement for the front-end, with initial results demonstrating potential for 30% commissioning time reduction and proactive fault mitigation, providing a scalable blueprint for operating next-generation high-intensity accelerators.