Results for “operability” — Page 95

NASA NTRS2021

High-Density Automated Vertiport Concept of Operations

The National Aeronautics and Space Administration (NASA) vision for Advanced Air Mobility (AAM) includes Urban Air Mobility (UAM) – a concept involving vertical takeoff and landing (VTOL) aircraft, decentralized (or federated) traffic management, and new infrastructure to support urban, suburban, and rural flight operations. High-density performance-based routes or corridors enable prompt transportation of people and goods from node to node, where each node represents a vertiport, defined as an identifiable ground or elevated area used for the takeoff and landing of VTOL aircraft. In the presence of uncertainty surrounding aircraft turnaround time on the ground, vertiports are the critical end points in scheduling, sequencing, and spacing (SSS) of aircraft in dense metropolitan environments. This Concept of Operations (ConOps) includes vertiports of varying sizes, configurations, service offerings, and locations. UAM air vehicles include conventional rotorcraft, unmanned VTOL aircraft, and novel piloted VTOL aircraft. This ConOps focuses on operations at a high-density vertiport, supported by a Vertiport Automation System (VAS) with high-throughput operation capabilities under conditions defined as NASA’s Urban Air Mobility Maturity Level Four (UML-4).

Urban Air Mobility↗

NASA NTRSDate not supplied

Uncrewed Lunar Surface Operations and Support Activities

This presentation summarizes the contents of the uncrewed lunar surface operations and support activities paper. It outlines the functions, features, and capabilities that support sustained human and robotic surface operations. It summarizes the complexities associated with the development and operation of autonomous systems and discusses a simulation tool for integrated surface operations planning.

Lunar↗

NASA NTRSDate not supplied

A Preliminary Development of The Intelligent Change Detection System (ICDS): Using Machine Learning to Combat Change Blindness in Remote Operation Environments

The emergence of Advance Air Mobility (AAM) will increase the number and types of aerial vehicles operating in shared airspaces, which will subsequently affect the amount of actionable data that ground control station operators (GCSOs) will be expected to manage. In this environment, GCSOs are particularly susceptible to the visual perceptual phenomenon known as change blindness, in which a stimulus undergoes a change without the change being noticed by its observer. A machine agent designed to detect change blindness and mitigate the effects could improve human agent performance in a data-saturated remote operations environment. The objective of this paper is to describe a proof-of-concept system architecture that integrates real-time eye tracking and vehicle telemetry data to prevent human-agent errors resulting from change blindness while operating aircraft from a remote ground control station (GCS).

Real-Time Streaming Data Architecture↗

NASA NTRSDate not supplied

Space Shuttle Program Dual Docked Operations

Due to the ever-increasing visiting vehicle traffic to and from the International Space Station (ISS) during mated Orbiter/ISS missions, it became apparent to both the ISS Program (ISSP) and the Space Shuttle program (SSP) that there would arise occasions where a visiting vehicle docking and/or undocking could overlap when the Space Shuttle Orbiter was docked to the ISS, referred to as Dual Docked Operations (DDO). The primary concerns were that plumes from the visiting vehicles would cause contamination, corrosion, and erosion to Space Shuttle surfaces and present a toxicity hazard to ground personnel when processing the Space Shuttle after landing. This potential conflict provided the genesis for evaluating risk mitigations to gain maximum flexibility for managing potential visiting vehicle traffic to and from the ISS and to maximize launch and landing opportunities for all visiting vehicles. The overarching goal of this effort was to assess the viability and readiness of the SSP to protect for DDO on any given mission. To assess the viability of performing DDO, the ISSP delivered plume particle fluence (i.e., number of particles per unit area) and contamination deposition based on the visiting vehicle’s proximity operations to the SSP for evaluation. This paper summarizes the concept definition, studies, and analysis results generated by the SSP, ISSP, and Mission Operations Directorate (MOD) for implementing DDO involving the Orbiter and Russian vehicles (RVs). Discussion and results presented herein represent a generic assessment of the SSP and ISSP integrated safety and technical evaluations of DDO involving the Orbiter and RVs. Mission-specific analysis and operational impacts were conducted flight-by-flight as they arose.

Launch vehicles↗

NASA NTRSDate not supplied

Space Shuttle Program Dual Docked Operations

- Dual Docked Operations (DDO) is the docking or undocking of a Russian Vehicle (RV) during Space Shuttle Orbiter/International Space Station (ISS) mated operations. - This paper and presentation summarizes the concept definition, studies, and analysis results generated by the Space Shuttle Program (SSP), ISS Program (ISSP), and Mission Operations Directorate for implementing DDO during mated Orbiter/ISS missions. [1] - Due to the ever-increasing visiting vehicle traffic to and from the ISS, it became apparent to both the ISSP and the SSP that there would arise occasions where conflicts between a visiting vehicle docking and/or undocking could overlap with a planned Space Shuttle launch and/or during mated Orbiter/ISS operations. - This potential conflict provided the genesis for evaluating risk mitigations to gain maximum flexibility for managing potential visiting vehicle traffic to and from the ISS and to maximize launch and landing opportunities for all visiting vehicles. - Reviews were conducted to assess the viability and readiness of the SSP to protect for DDO on any given mission and it was found to be t echnically feasible

Launch vehicles↗

NASA NTRS2023

Space Shuttle Program Dual Docked Operations

- Dual Docked Operations (DDO) is the docking or undocking of a Russian Vehicle (RV) during Space Shuttle Orbiter/International Space Station (ISS) mated operations. - This paper and presentation summarizes the concept definition, studies, and analysis results generated by the Space Shuttle Program (SSP), ISS Program (ISSP), and Mission Operations Directorate for implementing DDO during mated Orbiter/ISS missions. [1] - Due to the ever-increasing visiting vehicle traffic to and from the ISS, it became apparent to both the ISSP and the SSP that there would arise occasions where conflicts between a visiting vehicle docking and/or undocking could overlap with a planned Space Shuttle launch and/or during mated Orbiter/ISS operations. - This potential conflict provided the genesis for evaluating risk mitigations to gain maximum flexibility for managing potential visiting vehicle traffic to and from the ISS and to maximize launch and landing opportunities for all visiting vehicles. - Reviews were conducted to assess the viability and readiness of the SSP to protect for DDO on any given mission and it was found to be t echnically feasible

Launch vehicles↗

NASA NTRSDate not supplied

A Preliminary Development of The Intelligent Change Detection System (ICDS): Using Machine Learning to Combat Change Blindness in Remote Operation Environments

The emergence of Advance Air Mobility (AAM) will increase the number and types of aerial vehicles operating in shared airspaces, which will subsequently affect the amount of actionable data that ground control station operators (GCSOs) will be expected to manage. In this environment, GCSOs are particularly susceptible to the visual perceptual phenomenon known as change blindness, in which a stimulus undergoes a change without the change being noticed by its observer. A machine agent designed to detect change blindness and mitigate the effects could improve human agent performance in a data-saturated remote operations environment. The objective of this paper is to describe a proof-of-concept system architecture that integrates real-time eye tracking and vehicle telemetry data to prevent human-agent errors resulting from change blindness while operating aircraft from a remote ground control station (GCS).

Real-Time Streaming Data Architecture↗

NASA NTRSDate not supplied

ETM Industry: Tabletop Defining Notions for Cooperative Operating Practices

This tabletop plans to cover 3 main topics in regards to the research that NASA has conducted for High-E Traffic Management (ETM), as well as implementing it to stand up an ETM system in the Airspace Operations Lab to invite industry participants to join us in a collaborative evaluation at the end of 2023. (Or earlier for system architectural connectivity). Working to define the notions that go into Cooperative Operating Practices for a collaborative traffic management process, we will be showing and training to some of our early prototype concepts and discussing: 1. Operational Intent: We have built an OI generation tool and would like to discuss specifics to the requirements of OI size, update rates, and duration. 2. Strategic Conflict Detection: We have built a concept to detect overlapping OI’s and would like to discuss the output information; time horizon, geometry and confidence level of intersection. In addition we are looking at a service that would provide all users with additional information in regards to the probability of your actual flight intent getting within a specified separation envelope. 3. Cooperative Operating Practices (COPS): Specifically looking at COPs for strategic deconfliction, we are looking to industry to build the actual COPS based on various criteria; vehicle-to-vehicle interactions, or company to company agreements. What is the strategy when OIs intersect; wait and see, take a pre-negotiated COPS action, or ad-hoc negotiation and discuss what that looks like.

High-E Traffic Management (ETM)↗

NASA NTRSDate not supplied

Trajectory Operations of the Artemis I Mission

This paper describes the operational design and execution of the Artemis I trajectory. It was an operationally complex trajectory with powered lunar flybys and insertion into a Distant Retrograde Orbit (DRO). A joint team of trajectory analysts at the NASA Johnson Space Center (JSC) were responsible for the design and operation of nominal and off-nominal in-space trajectories. A process was developed to convert optimized reference trajectories into Orion burn plans that could be uplinked to the vehicle. During the mission, the joint flight controller and engineering team continuously evaluated upcoming translational burns using actual vehicle conditions, monitored the trajectory for opportunities to re-optimize the trajectory in order to reduce propellant usage, and prepared for potential off-nominal scenarios. Overall Orion in-space trajectory performance is compared tomission designs to demonstrate the success of the design and operations work-flows.

Artemis I↗

NASA NTRSDate not supplied

Long-Term Orbit Operations for the Lunar Reconnaissance Orbiter

The Lunar Reconnaissance Orbiter (LRO) has been operating for 14 years at the Moon after it was launched on its original one-year mission on June 18, 2009. Along with the unprecedented scientific record, operating LRO has revealed many significant factors re-lated to orbit operations that can be used when designing future low-lunar orbit missions. Among other things, these factors include maintaining a low-lunar orbit, applications of a frozen orbit, observing a drift in the orbit inclination, compensating operations to survive through long lunar eclipses, and developing inventive new ways to execute maneuvers.

LRO Orbit Dynamics Operations↗

NASA NTRSDate not supplied

Long-Term Orbit Operations for the Lunar Reconnaissance Orbiter

The Lunar Reconnaissance Orbiter (LRO) has been operating for 14 years at the Moon after it was launched on its original one-year mission on June 18, 2009. Along with the unprecedented scientific record, operating LRO has revealed many significant factors re-lated to orbit operations that can be used when designing future low-lunar orbit missions. Among other things, these factors include maintaining a low-lunar orbit, applications of a frozen orbit, observing a drift in the orbit inclination, compensating operations to survive through long lunar eclipses, and developing inventive new ways to execute maneuvers.

LRO Orbit Dynamics Operations↗

NASA NTRSDate not supplied

UTM Public Operators Outreach: Drone Responders

NASA is working with Industry and FAA to enable beyond visual line of sight (BVLOS) unmanned aerial systems (UAS) operations wherever it is needed. The purpose of this presentation is to widen NASA's reach of Public Operator involvement in the UTM BVLOS space and inform them of the fundamental principles of UTM/BVLOS. Collaboration between NASA and Public Operations is needed to help define how services can be approved and applied to enable BVLOS operations in the nation's airspace.

UTM↗

NASA NTRS2024

Architecture of High-Altitude Operations (HAO) Discovery and Synchronization Service (DSS)

The aviation industry is evolving at an unprecedented pace, necessitating the development of efficient, secure, and interoperable systems to manage increasingly complex air traffic. Moreover, the demand for High-Altitude Operations (HAO) is increasing. Furthermore, air traffic control services are limited in HAO environments. HAO industry participants will need airspace access and flexibility to perform their missions in this airspace that provides provisions for scalability. The Discovery and Synchronization Service (DSS) will be a cornerstone of the HAO ecosystem, enabling the effective sharing of critical airspace data, including operational intent, aircraft trajectories, and airspace usage among various stakeholders and operators. The DSS architecture addresses these challenges with a distributed, decentralized, and interoperable system that facilitates seamless integration across diverse airspaces. It prioritizes secure data exchange while safeguarding data ownership. This white paper presents the vision, architecture, and benefits of the DSS for HAO, underscoring its potential to streamline operations, reduce redundancies, and establish a foundation for safe and efficient airspace management.

HAO↗

DOE OSTI2022

Changes in Hydropower Resource Operations Following Participation in the CAISO Energy Imbalance Market - A Case Study

Portland General Electric (PGE), an investor-owned electric utility serving nearly 900,000 customers in 51 Oregon cities, joined Western Energy Imbalance Market (EIM) – a California Independent System Operator (CAISO) platform for sharing cleaner and more efficient generation resources among multiple states in the Western U.S. While participation in EIM brings a new set of economic opportunities for PGE by capturing flexibility needs in the market, it also changes operational patterns (e.g., more frequent start/stop, off-nominal power output, and ramping) of PGE’s conventional generation fleet, including hydropower units. With 492 MW of hydropower capacity, which constitutes 15\% of PGE’s generation mix, it is important for PGE (as any other EIM-participant utility) to understand how participation in EIM impacts its hydropower assets, and how a techno-economically sustainable operation of those assets could be accomplished. In this paper, a case study is performed with PGE's hydropower generation facilities to assess and quantify EIM-participation driven changes in hydropower operational patterns.

hydropower, operational patterns, energy imbalance↗

DOE OSTI2023

Applying Cyber-Informed Engineering to Power System Operations

This presentation covers the interaction of the discipline of system operations with the growing body of knowledge around Cyber-Informed Engineering (CIE). First is discussion of a number of fundamental concepts for system operators - organizational division of responsibilities, human and machine cooperation, goals, and priorities. The next section covers the reasons why CIE was developed, what it is, and the key design and operational, and organizational principles of CIE. Finally, some thoughts on how CIE can be applied to power system operations are offered, along with some examples of how an organization can approach applying CIE principles in their particular circumstances.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

GOOML - Real World Applications of Machine Learning in Geothermal Operations

GOOML (Geothermal Operational Optimization with Machine Learning) is a machine-learning based framework that enables geothermal power plant operators to explore optimization opportunities for their assets in an efficient and robust digital environment. Backed by real-world data sources, thermodynamic constraints and steamfield intelligence, the GOOML environment provides new tools to explore how to best operate steamfields as well as test new scenarios and configurations prior to implementation in the field. To prove the effectiveness of GOOML, we have undertaken optimization experiments using reinforcement learning (RL) to generate operational suggestions using a balance of mass-take targets, sustainability considerations and net generation. Our experiments use the GOOML construct to explore different field parameters and perform multiple reinforcement learning experiments. Like a comprehensive laboratory workbench, we can change out components of a steamfield to perform testing under a variety of conditions (restrict mass, increase pressure, reroute steam, etc.). This flexibility allows us to explore conditions that would require significant infrastructure changes in a real-world setting at a fraction of the cost and time in a digital environment. The results highlight the benefits of using digital twins and advanced data analytics for the geothermal industry.

forecasting↗

DOE OSTI2023

AI-ENABLED MONITORING OPTIONS TOWARDS SECURE MICROREACTOR DEPLOYMENT AND OPERATIONS

Global nuclear energy deployment scenarios suggest favorable economics for smaller, more versatile, and self-contained reactor technologies. Recognizing their key features as integrated, autonomous and either semi-remotely operated or fully remotely operated systems, microreactors are expected to be deployed in large numbers servicing off/micro-grids, many in geographically remote locations. Integrated nature of these systems as well as ease of their transportation as complete units, simplified installation and relocation/decommissioning challenge traditional continuity-of-knowledge practices used for conventional light water reactors where refueling is done onsite at designated times only replacing portions of their cores and only after their full commissioning for operations including completion of their containment building with security and safeguards measures in place. This effort is exploring AI (artificial intelligence)-enabled monitoring options that would be design agnostic and would assure secure unit deployment and operations. The principle is to maintain situational awareness via real-time evaluations of simultaneous and remotely transmitted monitoring data capturing key safeguards attributes including such characteristics as temperature, radiation, vibrational signals (inter alia), and others. The key principle is to provide reliable and resilient security options while maintaining simplified and economical deployment. The paper will review feasible options for AI-enabled solutions for such evaluations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.

97 MATHEMATICS AND COMPUTING↗