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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.

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At least 415 records · Page 23

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NASA NTRS2013

Characterization of Swirl-Venturi Lean Direct Injection Designs for Aviation Gas-Turbine Combustion

Injector geometry, physical mixing, chemical processes, and engine cycle conditions together govern performance, operability and emission characteristics of aviation gas-turbine combustion systems. The present investigation explores swirl-venturi lean direct injection combustor fundamentals, characterizing the influence of key geometric injector parameters on reacting flow physics and emission production trends. In this computational study, a design space exploration was performed using a parameterized swirl-venturi lean direct injector model. From the parametric geometry, 20 three-element lean direct injection combustor sectors were produced and simulated using steady-state, Reynolds-averaged Navier-Stokes reacting computations. Species concentrations were solved directly using a reduced 18-step reaction mechanism for Jet-A. Turbulence closure was obtained using a nonlinear κ-ε model. Results demonstrate sensitivities of the geometric perturbations on axially averaged flow field responses. Output variables include axial velocity, turbulent kinetic energy, static temperature, fuel patternation and minor species mass fractions. Significant trends have been reduced to surrogate model approximations, intended to guide future injector design trade studies and advance aviation gas-turbine combustion research.

Combustion Chamber↗

NASA NTRS2016

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

anomaly detection↗

NASA NTRS2017

Assessment of a Conceptual Flap System Intended for Enhanced General Aviation Safety

A novel multielement trailing-edge flap system for light general aviation airplanes was conceived for enhanced safety during normal and emergency landings. The system is designed to significantly reduce stall speed, and thus approach speed, with the goal of reducing maneuveringflight accidents and enhancing pilot survivability in the event of an accident. The research objectives were to assess the aerodynamic performance characteristics of the system and to evaluate the extent to which it provided both increased lift and increased drag required for the low-speed landing goal. The flap system was applied to a model of a light general aviation, high-wing trainer and tested in the Langley 12- Foot Low-Speed Wind Tunnel. Data were obtained for several device deflection angles, and component combinations at a dynamic pressure of 4 pounds per square foot. The force and moment data supports the achievement of the desired increase in lift with substantially increased drag, all at relatively shallow angles of attack. The levels of lift and drag can be varied through device deflection angles and inboard/outboard differential deflections. As such, it appears that this flap system may provide an enabling technology to allow steep, controllable glide slopes for safe rapid descent to landing with reduced stall speed. However, a simple flat-plate lower surface spoiler (LSS) provided either similar or superior lift with little impact on pitch or drag as compared to the proposed system. Higher-fidelity studies are suggested prior to use of the proposed system.

Campbell, Bryan A.↗

NASA NTRS2020

Changes in Frequency of the Severity and Type of Aviation Accidents (1987 to 2016)

This document reports the fourth analysis to identify the types of accidents with the greatest impact on the overall safety risk in U.S. civil aviation. The first three analyses examined accidents in 1997-20061, in 2001-20102 and in 2005-20143. The safety risks herein are defined to include four elements: (1) the number of total accidents; (2) the number of fatal accidents; (3) the number of total injuries; (4) the number of fatal injuries. Two of the previous analyses also included the number of incidents, but incident data since 2007 are difficult to separate by flight operation. Other incident details are no longer recorded as well. Due to these changes, the author’s working copy of the incident data has not been updated since 2011 (for incidents through 2010), and the incident data were not included in this analysis. Powerplant and non-powerplant system component failures were the only categories with a greater contribution to the overall safety risk from incidents than from accidents. Accident types, as well as accident rates, have been shown to vary considerably among different flight operations (e.g., large air carriers versus general aviation). For this reason, all analyses were done separately for four types of flight operations (Part 121, Scheduled Part 135, Non- Scheduled Part 135 and Part 91).

Evans, Joni K.↗

NASA NTRS2019

Overview of Liner Activities in Support of the International Forum for Aviation Research

This paper provides an overview of liner research being conducted by members of the International Forum for Aviation Research (IFAR). The IFAR consists of representatives from a number of national research labs and is established to enable information exchange on aviation research activities. NASA has provided three challenges that explore different aspects of acoustic liner research, and will compile the results from each of the partners into a common database when the challenges are completed. The goal of the first challenge is to compare experimental methods applied at different national labs for acoustic liner evaluation, and to compare impedance eduction methodologies for uniform liners. The second challenge is to evaluate the ability of 3D propagation codes to predict the effects of spanwise variable-impedance liners on the acoustic pressure field. The third challenge is to explore different approaches to simultaneously educe the impedances for each zone of a three-zone liner. Each challenge is intended to be conducted as a blind test. Thus, only representative results achieved by the NASA Liner Physics Team for each of the three challenges are presented herein. When the results from all participants are available, the compiled results are intended to be provided in future reports.

Jones, Michael G.↗

NASA NTRSDate not supplied

Augmenting Topic Finding in the NASA Aviation Safety Reporting System using Topic Modeling

Context: The NASA Aviation and Safety Reporting System (ASRS) provides various publications to the aviation community (including individual anonymous reports, Callback, Database Search Requests, Directline, and Alerting Messages). Key to these publications are the timely processing of new reports, which is currently done mostly manually by ASRS staff, and which the volume increases yearly. Aim: We investigate whether existing topic modelling techniques are suitable to ease some of the manual effort, and to enhance it with additional visual cues regarding the process of grouping, sense making and labeling incoming (and previous) reports. Method: We evaluate the applicability of WarpLDA topic modelling results combined with three visualization tools, the first two of which have been extended by us in this work for ASRS: Termite, TopicFlow, and LDAVis. Based on the identified limitations in these tools, we propose a methodology for improving them, and evaluate their outputs using ASRS as our test dataset. Results: The user interfaces of Termite, Topicflow and LDAVis were found insufficient for sense-making of the narratives. Moreover, concerns regarding the stability of results due to the inherent randomness of topic modelling, and the lack of a measurable approach for evaluation against the existing ASRS manual workflow were also noted. Conclusion: While many tools to topic modeling and visualization have been proposed, more work is necessary before they can be applied in practical situations to improve existing manual workflows. The methodology presented and applied in this work contribute towards this effort.

ASRS↗

NASA NTRSDate not supplied

Examining the Changing Roles and Responsibilities of Humans in Envisioned Future In-Time Aviation Safety Management Systems

Advances in technology are enabling new concepts of operations that will trans-form aviation including increasingly autonomous capabilities to handle evolving complex dynamic ecosystems like those associated with Advanced Aerial Mobility. A major challenge is how to ensure today’s safety levels are maintained as the system scales for rapid detection and timely mitigation of safety issues. NASA has developed a concept of operation for In-Time Aviation Safety Management Systems (IASMS) that represents a system-of-system perspective on interconnected capabilities needed to proactively reduce risk in complex operational environments where unknown hazards may exist. As a result, NASA research priorities include under-standing how the balance between humans and automation changes in such envisioned systems, which may lead to novel human-machine interaction paradigms and human-autonomy teaming for informed contingency management.

Lawrence Prinzel↗

NASA NTRSDate not supplied

Community Noise Assessment of Urban Air Mobility Vehicle Operations using the FAA Aviation Environmental Design Tool

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate in communities close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration’s Aviation Environmental Design Tool (AEDT), does not support analysis of such operations due to a combined lack of a UAM aircraft performance model and aircraft noise data. This paper discusses the initial development of a method to assess the acoustic impact of UAM fleet operations on the community using AEDT and demonstrates its use for representative UAM operations. In particular, methods were developed using fixed-point flight profiles and user-supplied noise data in a manner that avoids unwanted behavior in AEDT. A set of 32 routes in the Dallas-Ft. Worth area were assessed for single and multiple (fleet) operations for two concept vehicles.

urban air mobility↗

NASA NTRSDate not supplied

Are we ready for the first EASA guidance on the use of ML in Aviation?

NASA has been working for the past 12 years on software tools for the assurance of software in Aviation critical systems. For now two years, NASA has focused more on the use of AI-based techniques in Aviation than the traditional software systems used in the past. The primary focus has been on machine learning (ML), and more specifically, on supervised off-line learning ML systems. NSA’s research has been driven by case studies such as a vision-based centerline tracking system (implemented using deep neural networks) and the new generation of collision avoidance systems developed under the FAA guidance, i.e., the family of ACAS-X products. Since EASA has recently released its first usable guidance for Level 1 machine learning applications, it is opportunity to see how the research done at NASA is mapping to this first guidance for ML. In this talk I will use the EASA guidance document as a guide to present the past, present, and future tools and techniques being developed at NASA. The intent is to not only provide an overview of the research effort at NASA but also to see how this effort is addressing the concerns listed in the EASA first usable guidance for ML.

Guillaume Brat↗

NASA NTRSDate not supplied

Community Noise Assessment of Urban Air Mobility Vehicle Operations using the FAA Aviation Environmental Design Tool

urban air mobility↗

NASA NTRSDate not supplied

Second-Generation UAM Community Noise Assessment Using the FAA Aviation Environmental Design Tool

Vehicles serving the urban air mobility (UAM) market are anticipated to operate in communities close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration’s Aviation Environmental Design Tool (AEDT), does not support analysis of such operations due to a combined lack of a UAM aircraft performance model and aircraft noise data. This paper discusses second-generation developments to assess the acoustic impact of UAM fleet operations on the community using AEDT and demonstrates its use for representative UAM operations. In particular, methods were developed to add broadband self noise into computed noise-power-distance data, and vertiport-centric operations were evaluated for two concept vehicles.

urban air mobility↗

NASA NTRS2022

NASA's System-Wide Safety...A New Era in Aviation is Here!

NASA’s System-Wide Safety project is developing the concept and requirements for an assured In-Time Aviation Safety Management System (IASMS) that will: Improve the efficiency of flight, the access to airspace, and the expansion of services provided by air vehicles; Explore, discover, and understand the impact on safety from growing complexity introduced by industry modernization; Develop and demonstrate innovative solutions that enable the aviation transformation envisioned for global airspace system; Identify and proactively mitigate risks in accordance with target levels of safety; Assure safe, rapid, and repeatable access to a transformed National Airspace System (NAS)

Kyle Ellis↗

NASA NTRSDate not supplied

Human Interfaces and Management of Information (HIMI) Challenges for “In-time” Aviation Safety Management Systems (IASMS)

The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.

Lawrence J Prinzel↗

NASA NTRSDate not supplied

An Overview of NASA Sustainable Aviation

An overview of NASA Aeronautics plans for sustainable aviation research is provided. Specific research activities for subsonic transports under the Sustainable Flight National Partnership (SFNP) are discussed as well as a preview of the Advanced Aircraft for Sustainable Aviation (AACES) 2050 solicitation.

James D Heidmann↗

NASA NTRSDate not supplied

Investigation of Intelligent Resource Management for Aviation Communications

The emergence of new aerial vehicles into the airspace as part of new initiatives, such as Advanced Air Mobility (AAM), will place growing demand for spectrum resources to support airspace operations. The traditional approach of using fixed channel allocations within standard service volumes will not allow for dynamic and efficient distribution of resources based on airspace demand; consequently, a new approach to aviation spectrum management will be required to meet the anticipated needs of airspace users. The National Aeronautics and Space Administration (NASA) is investigating the application of advanced concepts to implement a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the quality of service prescribed by aeronautical standards. This technical investigation evaluates the dynamic assignment of resources for both air-ground and air-air communication links applicable to both the emerging AAM initiative as well as the existing air traffic management system. The performance of the proposed spectrum management concepts will be evaluated using a custom modeling and simulation capability that is currently under development. The implementation of these approaches is anticipated to facilitate increased spectrum utilization efficiency and enhanced airspace capacity, which will better serve the needs of future applications.

Aeronautics↗

NASA NTRSDate not supplied

Investigation of Intelligent Resource Management for Aviation Communications

Eric Knoblock↗

NASA NTRSDate not supplied

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗

NASA NTRS2023

Concept of Operations for an In-time Aviation Safety Management System (IASMS) for Upper E Airspace

The National Airspace System undergoes continuous change including in the Upper Class E airspace involving increasingly complex operations and a widening diversity of vehicles. To secure a safe future system, the National Academies recommended an In-time Aviation Safety Management System (IASMS) that is extensible to Upper E. Current Air Traffic Management is not cost-effective to scale for future Upper E operations and diversity of vehicles so the Federal Aviation Administration developed an Upper E Traffic Management ConOps to safely integrate the diverse operations and vehicles having different performance characteristics and flight missions without disrupting current operations including space launch and reentry, suborbital flights, supersonic and hypersonic flights, slow moving or stationary unmanned balloons, and long endurance fixed wing vehicles that are slow, stationary, or high speed. IASMS integrates state-of-the-art predictive modeling with reactive and proactive analytics to detect hazards and mitigate risk precursors for Upper E operators. IASMS identifies emergent safety risks exposed by transformation of the NAS with new and increasingly complex operations. Safety intelligence will also expand the data available and offer insight to new approaches for implementing safety improvements to mitigate risk with more seamless “in-time” integration across the policy, risk management, safety assurance, and promotion pillars of SMS.

K Ellis↗