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Natalia Alexandrov

Publications and source records attributed to Natalia Alexandrov.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations

Flight testing has been the historical standard for determining aircraft airworthiness. However, increases in the cost of flight testing and the accuracy of inexpensive CFD encourage the adoption of certification by analysis to reduce or replace flight testing. A framework is introduced to predict the performance in the special case of a modification to an existing, previously certified aircraft. This framework uses a combination of existing flight tests or high fidelity data of the original aircraft as well as lower fidelity data from CFD or wind tunnel testing of the original and modified configurations to create 6-DOF flight dynamics models. Two methods are presented which generate an updated flight dynamics model and estimate the model form uncertainty for the modified aircraft configuration using knowledge of the original aircraft. This updated dynamics model and uncertainty estimate are then used to conduct non-deterministic simulations with wind turbulence included. The framework is applied to an example aircraft system to demonstrate the ability to predict the performance and associated model from the uncertainty of modified aircraft configurations.

uncertainty quantification↗
NASA NTRS2021

ATTRACTOR: Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability

Autonomous systems (AS) are crucial to realizing the vision of new, complex transportation modes, such as advanced air mobility (AAM) and urban air mobility (UAM). A chief barrier to induction of AS into aviation is insufficient understanding of AS reliability in time-critical and safety-critical environments—an obstacle to certification. ATTRACTOR is aimed at building a basis for certification of classes of autonomous cyber-physical-human systems (CPHS) via establishing metrics and models of trustworthiness and trust in multi-agent team interactions, analyzable trajectories, explainability of computational algorithms (explainable artificial intelligence, or XAI), and persistent modeling and simulation, in the context of missions planning and operation. By “building a basis for certification,” we mean acquiring an understanding of when a system is trustworthy and developing computable means to estimate trustworthiness and trust in order to eventually inform functional requirements that contribute to certification. The outcomes are applicable not just to aviation but to all domains that rely on autonomous systems.

Autonomy↗
NASA NTRS2020

State of the Profession Considerations: NASA Langley Research Center Capabilities / Technologies for Autonomous In-Space Assembly and Modular Persistent Assets

Successfully implementing OSAM into next generation revolutionary observatories requires integrating expertise and technologies in modular space structures, assembly operations, autonomy, and modeling/simulation. LaRC OSAM technologies/capabilities have been presented to inform the Planetary Science and Astrobiology Decadal Survey community of the robust and mature existing capability to support an OSAM based architecture for their next observatory. LaRC Structures and Assembly capabilities enable; a modular telescope architecture, high-performance structural modules, and robotic assembly techniques. LaRC Autonomy capabilities ensure that the robotic assembly will be accomplished in a safe and robust manner and only require humans in a supervisory role. The LaRC toolbox of Modeling and Simulation capabilities that is calibrated using module-level ground testing, will ensure that the performance of the fully assembled observatory, a very large zero-g system that will never be assembled/tested in a gravity environment, meets all performance requirements when it enters into service. Integrating all three LaRC capabilities and including embedded metrology, will enable servicing, repair, instrument upgrades (and/or replacement) while ensuring a very long lifetime for the observatory and providing a return-on-investment that is substantially greater than the initial cost. Further confidence will be achieved as OSAM technologies are validated in a new LaRC OSAM laboratory that allows large-scale collaborative testing of modular hardware, simulation software and algorithms, and autonomous agents.

Large space structures↗
NASA NTRS2011

TPSAS-NF1676L-12321-DND

The operation of some networks, such as air transportation networks, can be complicated by congestion through a small subset of nodes. The congestion may be influenced by the connectivity of the network, or by the presence of constraints restricting the flow through particular nodes. This work investigates the effects of both connectivity and node flow constraints on the operation of a network. We develop the Minimax Node Load Problem (MNLP), a multicommodity flow model which minimizes the worst-case flow through any node in a given input network. The optimal solution to this problem provides us with the minimax node load, which we propose as a measure of network congestion. Keeping the number of nodes fixed, we first increase connectivity in a series of networks, and observe that topologies with more distributed connections result in a reduction in the minimax node load. However, when connectivity is increased further, the reductions diminish and are accompanied by solutions with undesirable qualities such as longer commodity paths. We then perform a second set of experiments over the same network, constraining flow through different subsets of nodes at different magnitudes of flow restriction, finding that (1) more constrained nodes lead to the largest increases in minimax node load and (2) constraints on the most connected nodes have the greatest effect on both congestion and commodity path length.

Douglas W Lee↗
NASA NTRS2011

TPSAS-NF1676L-12264-DND

The operation of some networks, such as air transportation networks, can be complicated by congestion through a small subset of nodes. The congestion may be influenced by the connectivity of the network, or by the presence of constraints restricting the flow through particular nodes. This work investigates the effects of both connectivity and node flow constraints on the operation of a network. We develop the Minimax Node Load Problem (MNLP), a multicommodity flow model which minimizes the worst-case flow through any node in a given input network. The optimal solution to this problem provides us with the minimax node load, which we propose as a measure of network congestion. Keeping the number of nodes fixed, we first increase connectivity in a series of networks, and observe that topologies with more distributed connections result in a reduction in the minimax node load. However, when connectivity is increased further, the reductions diminish and are accompanied by solutions with undesirable qualities such as longer commodity paths. We then perform a second set of experiments over the same network, constraining flow through different subsets of nodes at different magnitudes of flow restriction, finding that (1) more constrained nodes lead to the largest increases in minimax node load and (2) constraints on the most connected nodes have the greatest effect on both congestion and commodity path length.

Douglas Lee↗
NASA NTRS2011

TPSAS-NF1676L-12301-DND

This work deals with performance properties of a dynamic traffic model, the Air Traffic Monotonic Lagrangian Grid (ATMLG), which can be used to evaluate new control strategies for conflict avoidance, separation assurance, and traffic management. The model is based on an algorithm and data structure called the Monotonic Lagrangian Grid (MLG), originally developed at NRL in the mid 1980s and since then used as an underpinning for various particle dynamics simulations. The MLG stores positions and other data needed to describe N moving objects, where N can be very large. The MLG algorithm involves sorting and ordering objects. A stationary grid is an alternative to the dynamic grid of MLG. Stationary grids can be attractive in that they do not require sorting. We investigate and report on the relative performances of air traffic simulations based on dynamic (MLG) and static (lat-long) grids.

Carolyn Kaplan↗
NASA NTRSDate not supplied

TPSAS-NF1676L-34630-DND

Explore the source record for details and available documents.

Natalia Alexandrov↗
NASA NTRSDate not supplied

TPSAS-NF1676L-23309-DND

Explore the source record for details and available documents.

Natalia Alexandrov↗
NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa R Le Vie↗
NASA NTRSDate not supplied

Exploring Multimodal Interactions in Human-Autonomy Teaming Using a Natural User Interface

The creation of a multimodal, natural user interface to facilitate multi-agent interaction is essential to establishing trust among human and machine teammates in multi-agent systems. Trust is being researched, along with trustworthiness, as a path to certification of autonomous systems by the Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) project at NASA. The Autonomous Mission Experimental Logistics Interactive Assistant (AMELIA) is a natural user interface that enables multimodal interaction and is designed for rapid mission planning. AMELIA is an intelligent system that considers the user’s preferred communication strategies, as well as the time-critical aspect of the multi-agent system decision-making process. Twenty-four participants planned a multi-agent search and rescue mission, with the aid of an intelligent assistant. The results show that while the combined use of touch and speech was faster than speech alone, the single modality, touch, was still the most efficient. Future research should investigate additional input technologies.

Lisa Renee Le Vie↗
NASA NTRSDate not supplied

Applicability of a Framework for Estimating Performance and Associated Uncertainty for Modified Aircraft Configurations

As improvements are made to the accuracy and reliability of modeling and simulation techniques, certification by analysis becomes a more attractive alternative compared to traditional aircraft flight testing. Certification by analysis is especially cost-effective when one considers modifications to a previously certified aircraft. However, it is important that the models and methods used are applicable and accurate throughout the intended use domain. A framework for estimating the performance and associated uncertainty was introduced in an earlier paper. The factors and limitations of this framework for estimating the performance and associated model form uncertainty are explored to determine the range of applicability of the framework, particularly with respect to model form, process and sensor noise, and quality of available flight test data. This paper focuses on the general limitations and applicability of the framework and not the applicability of the individual methods to a range of modified configurations, which requires a large number of modified configurations and is an area for future work. The effects of these factors on the performance and uncertainty results are demonstrated using NASA’s Generic Transport Model aircraft.

uncertainty quantification↗