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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS2009

Baseline Assessment and Prioritization Framework for IVHM Integrity Assurance Enabling Capabilities

Fundamental to vehicle health management is the deployment of systems incorporating advanced technologies for predicting and detecting anomalous conditions in highly complex and integrated environments. Integrated structural integrity health monitoring, statistical algorithms for detection, estimation, prediction, and fusion, and diagnosis supporting adaptive control are examples of advanced technologies that present considerable verification and validation challenges. These systems necessitate interactions between physical and software-based systems that are highly networked with sensing and actuation subsystems, and incorporate technologies that are, in many respects, different from those employed in civil aviation today. A formidable barrier to deploying these advanced technologies in civil aviation is the lack of enabling verification and validation tools, methods, and technologies. The development of new verification and validation capabilities will not only enable the fielding of advanced vehicle health management systems, but will also provide new assurance capabilities for verification and validation of current generation aviation software which has been implicated in anomalous in-flight behavior. This paper describes the research focused on enabling capabilities for verification and validation underway within NASA s Integrated Vehicle Health Management project, discusses the state of the art of these capabilities, and includes a framework for prioritizing activities.

Cooper, Eric G.↗
NASA NTRS2008

Emergency Landing Planning for Damaged Aircraft

Considerable progress has been made over the last 15 years on building adaptive control systems to assist pilots in flying damaged aircraft. Once a pilot has regained control of a damaged aircraft, the next problem is to determine the best site for an emergency landing. In general, the decision depends on many factors including the actual control envelope of the aircraft, distance to the site, weather en route, characteristics of the approach path, characteristics of the runway or landing site, and emergency facilities at the site. All of these influence the risk to the aircraft, to the passengers and crew, and to people and property on the ground. We describe an ongoing project to build and demonstrate an emergency landing planner that takes these various factors into consideration and proposes possible routes and landing sites to the pilot, ordering them according to estimated risk. We give an overview of the system architecture and input data, describe our preliminary modeling of risk, and describe how we search the space of landing sites and routes.

Meuleau, Nicolas↗
NASA NTRS2009

Validation through Full-Scale Flight Exploration

Objectives: a) Regain Stable Platform: 1) Metrics analogous to stability margins needed for adaptive control systems; 2) Avoid adverse structural interactions. b) Maneuverability: 1) Control vehicle within new constraints; 2) Respect structural limitations; 3) Inform pilot of new performance limitations. c) Provide ability to safely land airplane: 1) Develop safest recovery trajectory.

Bosworth, John T.↗
NASA NTRS2009

Construction of Low Dissipative High Order Well-Balanced Filter Schemes for Non-Equilibrium Flows

The goal of this paper is to generalize the well-balanced approach for non-equilibrium flow studied by Wang et al. [26] to a class of low dissipative high order shock-capturing filter schemes and to explore more advantages of well-balanced schemes in reacting flows. The class of filter schemes developed by Yee et al. [30], Sjoegreen & Yee [24] and Yee & Sjoegreen [35] consist of two steps, a full time step of spatially high order non-dissipative base scheme and an adaptive nonlinear filter containing shock-capturing dissipation. A good property of the filter scheme is that the base scheme and the filter are stand alone modules in designing. Therefore, the idea of designing a well-balanced filter scheme is straightforward, i.e., choosing a well-balanced base scheme with a well-balanced filter (both with high order). A typical class of these schemes shown in this paper is the high order central difference schemes/predictor-corrector (PC) schemes with a high order well-balanced WENO filter. The new filter scheme with the well-balanced property will gather the features of both filter methods and well-balanced properties: it can preserve certain steady state solutions exactly; it is able to capture small perturbations, e.g., turbulence fluctuations; it adaptively controls numerical dissipation. Thus it shows high accuracy, efficiency and stability in shock/turbulence interactions. Numerical examples containing 1D and 2D smooth problems, 1D stationary contact discontinuity problem and 1D turbulence/shock interactions are included to verify the improved accuracy, in addition to the well-balanced behavior.

Wang, Wei↗
NASA NTRS2011

A Piezoelectric PZT Ceramic Mulitlayer Stack for Energy Harvesting Under Dynamic Forces

Piezoelectric energy harvesting transducers (PEHTs) are commonly used in motion/vibration energy scavenging devices. To date, most researchers have focused on energy harvesting at narrow bandwidths around the mechanical resonance frequency, and most piezoelectric harvesting devices reported in the literature have very low effective piezoelectric coefficient (d(sub eff)) (< 10(exp 4) pC/N). For instance, more than 80% of PEHT related papers are on transverse "31" mode cantilever beam type PEHTs (CBPEHTs) having piezoelectric coefficients of about 100 pC/N. The level of harvested electrical power for CBPEHTs is on the order of microW even at resonance mode. In order to harvest more electrical energy across broader bandwidth, high effective piezoelectric coefficient structures are needed. In this study, we investigate a "33" longitudinal mode, piezoelectric PZT ceramic multilayer stack (PZT-Stack) with high effective piezoelectric coefficient for high-performance PEHTs. The PZT-Stack is composed of 300 layers of 0.1 mm thick PZT plates, with overall dimensions of 32.4 mm X 7.0 mm X 7.0 mm. Experiments were carried out with dynamic forces in a broad bandwidth ranging from 0.5 Hz to 25 kHz. The measured results show that the effective piezoelectric coefficient of the PZT-stack is about 1 X 10(exp 5) pC/N at off-resonance frequencies and 1.39 X 10(exp 6) pC/N at resonance, which is order of magnitude larger than that of traditional PEHTs. The effective piezoelectric coefficients (d(sub eff)) do not change significantly with applied dynamic forces having root mean square (RMS) values ranging from 1 N to 40 N. In resonance mode, 231 mW of electrical power was harvested at 2479 Hz with a dynamic force of 11.6 N(sub rms), and 7.6 mW of electrical power was generated at a frequency of 2114 Hz with 1 N(sub rms) dynamic force. In off-resonance mode, an electrical power of 18.7 mW was obtained at 680 Hz with a 40 N(sub rms) dynamic force. A theoretical model of energy harvesting for the PZT-Stack is established. The modeled results matched well with experimental measurements. This study demonstrated that high effective piezoelectric coefficient structures enable PEHTs to harvest more electrical energy from mechanical vibrations or motions, suggesting an effective design for high-performance low-footprint PEHTs with potential applications in military, aerospace, and portable electronics. In addition, this study provides a route for using piezoelectric multilayer stacks for active or semi-active adaptive control to damp, harvest or transform unwanted dynamic vibrations into useful electrical energy.

Xu, Tian-Bing↗
NASA NTRS2012

An Integrated Framework for Model-Based Distributed Diagnosis and Prognosis

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

Bregon, Anibal↗
NASA NTRS2016

Using Powder Cored Tubular Wire Technology to Enhance Electron Beam Freeform Fabricated Structures

Electron Beam Freeform Fabrication (EBF3) is an additive manufacturing technique, developed at NASA Langley Research Center, capable of fabricating large scale aerospace parts. Advantages of using EBF3 as opposed to conventional manufacturing methods include, decreased design-to-product time, decreased wasted material, and the ability to adapt controls to produce geometrically complex parts with properties comparable to wrought products. However, to fully exploit the potential of the EBF3 process development of materials tailored for the process is required. Powder cored tubular wire (PCTW) technology was used to modify Ti-6Al-4V and Al 6061 feedstock to enhance alloy content, refine grain size, and create a metal matrix composite in the as-solidified structures, respectively.

Gonzales, Devon↗
NASA NTRS2016

Real-Time Global Nonlinear Aerodynamic Modeling for Learn-To-Fly

Flight testing and modeling techniques were developed to accurately identify global nonlinear aerodynamic models for aircraft in real time. The techniques were developed and demonstrated during flight testing of a remotely-piloted subscale propeller-driven fixed-wing aircraft using flight test maneuvers designed to simulate a Learn-To-Fly scenario. Prediction testing was used to evaluate the quality of the global models identified in real time. The real-time global nonlinear aerodynamic modeling algorithm will be integrated and further tested with learning adaptive control and guidance for NASA Learn-To-Fly concept flight demonstrations.

Morelli, Eugene A.↗
NASA NTRS2016

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗
NASA NTRS2018

NASA's Learn-to-Fly Project Overview

Learn-to-Fly (L2F) is an advanced technology development effort aimed at assessing the feasibility of real-time, self-learning flight vehicles. Specifically, research has been conducted on merging real-time aerodynamic modeling, learning adaptive control, and other disciplines with the goal of using this “learn to fly” methodology to replace the current iterative vehicle development paradigm, substantially reducing the typical ground and flight testing requirements for air vehicle design. Recent activities included an aggressive flight test program with unique fully autonomous fight test vehicles to rapidly advance L2F technology. This paper presents an overview of the project and key components.

Heim, Eugene H. D.↗
NASA NTRS2019

Autonomy @ NASA for Aeronautics

NASA has been exploring the use of artificial intelligence technologies to improve vehicle and airspace capabilities for over 20 years. This research has ranged from the use of neural nets to allow adaptive control, to autonomous rotorcraft operations, to real-time prognostics, to an eventual goal of autonomous vehicles operating in an autonomous airspace, in the context of smart communities. Examples of past research and future directions in autonomy for aeronautics will be presented.

Van Dalsem, William R.↗
NASA NTRS2019

MRAC with Time-Varying Reference Model from Convex Combination

An extension of model reference adaptive control is presented that accommodates use of a time-varying reference model. Specifically, the reference model is taken to be a time-varying convex combination of two linear, time-invariant models. The design is intended to act as a way to smoothly transition between two different reference models without resorting to a scheduled switch. It also provides the ability to use an interpolated reference model when the plant is operating between design points. The time variation of the combination must satisfy some requirements to ensure stability but is otherwise user choice. Subject to these requirements, bounded tracking error behavior is demonstrated via Lyapunov stability analysis for the single-input, single-output, output feedback case. Tracking error convergence is asymptotic when time variation ceases. The proposed design is demonstrated in simulation of a numerical model.

Hashemi, Kelley E.↗
NASA NTRS2014

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗
NASA NTRS2018

Practical Aspects of Real-Time Modeling for the Learn-To-Fly Concept

Practical aspects of autonomous onboard real-time modeling for the NASA Learn-to-Fly concept are examined using flight data from two subscale test aircraft. A practical real-time global nonlinear aerodynamic modeling method is developed and explained, along with the multiple-input excitation needed for effective real-time modeling. Critical issues for integrating real-time global nonlinear aerodynamic modeling and local linear modeling with the real-time learning adaptive control and guidance elements of the Learn-to-Fly algorithm are identified and discussed. Real-time modeling results from NASA Learn-to-Fly flight demonstrations are presented and evaluated using model fit metrics and prediction tests.

Morelli, Eugene A.↗
NASA NTRS2017

TPSAS-NF1676L-28077-DND

Learn-To-Fly merges real-time modeling, learning adaptive control, and real-time guidance to achieve: (1) autonomous self-learning aircraft to disrupt our current vehicle development paradigm, (2) novel configuration development with no ground testing, and (3) robust autonomous flight of novel aircraft.

Mark A Croom↗
NASA NTRSDate not supplied

Dynamic Vehicle Assessment for Intelligent Contingency Management of Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope↗
NASA NTRSDate not supplied

Dynamic Vehicle Assessment for Intelligent Contingency Management for Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope prediction↗