Search NASA⌕ Search

SEARCH · Search NASA

Results for “ASE”

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.

261 records · Page 15

Gust Load Alleviation Control and Gust Estimation for a High Aspect Ratio Wing Wind Tunnel Model

This paper presents the gust load alleviation (GLA) study of the aspect ratio 13.5 Common Research Model (CRM) wind-tunnel model. This study details the design of the GLA controller in preparation for wind-tunnel testing in the Transonic Dynamics Tunnel at NASA Langley Research Center. An aeroservolastic (ASE) model is first reduced using a model reduction method that takes advantage of the sinusoidal steady-state response. Then, the reduced model is used to design an extended-state Kalman filter which estimates the states and the sinusoidal gust input. The GLA control is then derived using the optimal control solution to a multi-objective cost function. The results of the GLA controller indicate a 69.07% reduction in wing-root strain without sensor noise and 68.45% reduction with sensor noise, while maintaining robust stability margins. An adaptive GLA controller is developed for uncertain gust frequency and shows a 71.03% reduction in wing root strain compared to the non-adaptive control reduction of just 39.19%.

Christopher Forte↗

The Heterogeneous Integration of Electronic Components

Heterogeneous integration (HI) of electronics components is broadly recognized as a powerful and crucial enabler for the continued growth of computing and communication. From 2010 onwards, the value of HI is increasingly visible in the advanced packaging used in artificial intelligence, high-performance computing, smartphones and communications product implementations. In this Perspective, we argue that HI is crucial to semiconductors and more broadly to the continued evolution of computing and communications. We use leading-edge advanced packaging examples to represent the value, advancements and opportunities for HI. To succeed, it is critical to develop comprehensive HI roadmaps that inform collaborations across the design, manufacturing and reliability spectrum between systems architects, packaging and semiconductor technologists to common goals. Although this article does not provide a full roadmap, we instead detail additional parameters for artificial intelligence, smartphone and other cellular communication devices, and their constituent building blocks including interconnects, power electronics, photonics, thermal management, reliability, modelling and co-design, to foster greater collaboration opportunities among academia, research laboratories and industry.

42 ENGINEERING↗

Safe Agents in Space: Lessons from the Autonomous Sciencecraft Experiment

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe these constraints and how they are reflected in our agent architecture.

Eatth Observing One Spacecraft↗

Lessons Learned from Autonomous Sciencecraft Experiment

An Autonomous Science Agent has been flying onboard the Earth Observing One Spacecraft since 2003. This software enables the spacecraft to autonomously detect and responds to science events occurring on the Earth such as volcanoes, flooding, and snow melt. The package includes AI-based software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software is in routine use to fly the EO-l mission. In this paper we briefly review the agent architecture and discuss lessons learned from this multi-year flight effort pertinent to deployment of software agents to critical applications.

Autonomous Sciencecraft Experiment (ASE)↗