TOPIC: A1 Aviation Safety

• Development of moisture-resistant resins and new surface treatments/primers. Novel chemistries are sought to improve the durability of aerospace adhesives with potential use on subsonic aircraft. This research opportunity is focused on the development of novel chemistries for coupling agents, surface treatments for adherends and their interfaces, leading to aerospace structural adhesives with improved durability. Work may involve chemical modification and testing of adhesives, coupling agents, surface treatments or combinations thereof and modeling to predict behavior and guide the synthetic approaches. Examples of adhesive characteristics to model and/or test may include, but are not limited to, hydrolytic stability of the interfacial chemistry, moisture permeability at the interface, and hydrophobicity of coupling agents and surface primers. Examples of adherends to model and/or test include carbon fiber/epoxy composites used in structural applications on subsonic aircraft, and aluminum, as well as their respective surface treatments.
  
• Concepts for autonomous self-healing of composite aerospace structures. NASA is interested only in passive approaches, i.e., approaches that do not require sensors or external energy to activate the healing process. Desired performance objectives include improved compression-after-impact performance and retarded/arrested damage growth. To be competitive with lightweight traditional (non-healing) aerospace structures, self-healing concepts must not introduce extensive passive weight, such as a reservoir tank of resin, etc.
  
• Test techniques to fully characterize aging history and strain rate effects on thermoset and/or thermoplastic resins as well as on advanced composites manufactured of such resins and reinforced with 3D fiber preforms such as the triaxial braid used in advanced composite fan containment structures.
  

• Intelligent systems monitoring and alerting technologies for improved failure mode identification, recovery, and threat mitigation;
  
• Designs for human-error prevention, detection, and mitigation;
  
• Support for crew response planning and selection;
  
• New sensors and/or new associated algorithms for determining operator states of attention, awareness, engagement, and intent;
  
• Approaches that appropriately modulate crew attention, engagement, workload, and situation awareness;
  
• Human-centered technologies to improve the performance of less-experienced operators and of pilots from special population groups;
  
• Human-error reliability approaches to analyzing flight deck displays, decision aids, procedures, and human/automation integration policies;
  
• Presentation and aiding concepts for the display and use of data with spatial or temporal uncertainty and of integrated streams of data with various levels of integrity;
  
• Naturalistic dialog approaches for interacting with aircraft systems and external agents in flight;
  
• Individual and team performance metrics, analysis methods, and tools to better evaluate and certify human and system performance for use in operational environments, simulation, and model-based analyses with focus on sequential behavior analysis.
  

• New and improved airborne forward-looking sensor systems;
  
• Data fusion technologies for integrating disparate sources of flight-related information with on-board and off-board sensor data to detect and evaluate aviation hazards;
  
• Innovative technologies and methods to detect, predict, and quantify hazards in order to provide accurate information and guidance to enable avoidance of hazards or to instigate strategies for mitigation; and
  
• Decision-support tools and methods to improve collaborative and distributive decision-making.
  

• Unmodeled dynamics (e.g., aeroelastic modes);
  
• Parametric uncertainty (e.g., stability and control derivative variations due to aerodynamic changes);
  
• Time-scale separation inherent in different actuators (e.g., slow engines as actuators);
  
• Nonlinear dynamic nature of the actuator response including time lag (e.g., engine variable spool-up time and actuator rate limiting);
  
• Stability of adaptive control methods in the presence of unmodeled dynamics and exogenous disturbances (e.g., wind shear and atmospheric turbulence).
  

• Multi-objective adaptive optimal control;
  
• Aeroservoelastic mode filtering adaptive control;
  
• Direct adaptive control;
  
• Indirect adaptive control;
  
• Hybrid (direct and indirect) adaptive control.
  

• Detect anomalies and faults;
  
• Detect trends;
  
• Discover similarities;
  
• Infer models from data;
  
• Detect topics from text;
  
• Classify instances or events;
  
• Fuse data from multiple sources;
  
• Display data mining results in an intuitive manner.
  

• Perform automated learning, both supervised and unsupervised;
  
• Permit the user to define the search criteria and heuristics;
  
• Support a mixed-initiative approach combing automated learning and user search control;
  
• Perform real-time analyses on continuous streams of data;
  
• Perform off-line analyses on static databases;
  
• Process one or more data types including numeric sequences, character sequences, English free-form text, image sequences, and combinations of these forms;
  
• Perform real-time analyses on continuous streams of data;
  
• Perform on-demand, scheduled, or triggered analyses on periodic and/or aperiodic data streams;
  
• Perform off-line analyses on static databases.
  

• Enhance diagnostic and prognostic capabilities of an onboard integrated health management system;
  
• Perform clustering and topic identification on reports from a Problem Reporting and Corrective Action system;
  
• Detect faults from image sequences;
  
• Enhance acceptance tests to reduce false positive and false negative classifications;
  
• Enhance information-based security systems by detecting anomalies;
  
• Improve the design process by discovering similar applicable designs given requirements;
  
• Support analyses that assess risk of component or system failure.
  

• 'Virtual' inspections on both monolithic homogeneous materials (i.e., metals) and composite materials using computational NDE tools. 'Virtual' inspections would include determining the size of flaws detectable with a particular technique, the parameters needed for inspections on a particular structure, or determining if a technique is applicable for a particular inspection. Techniques modeled could include (but are not limited to) terahertz imaging, thermography, ultrasonics, eddy current or radiographics.
  
• Chafing of wiring insulation is the primary reason for wire failure in both military and commercial aircraft. Computational methods are being solicited for analyzing data from nondestructive inspection techniques to detect and characterize chafing as early as possible, thus enabling useful life predictions.
  
• Hard shell composite fan containment components that include sandwich structures. Of interest are practical large-area rapid inspection and/or health monitoring methods that can monitor the bulk interior as well as the surface of the component over significant distances as the component goes through its service life. Techniques could include (but are not limited to) ultrasonic guided waves that interrogate the bulk while traveling laterally along the component surface, acoustic emission systems, and robust pressure-sensitive film systems that can visually record impacts and impact paths while surviving the service and impact conditions.
  
• Increased use of composite structure and components in aircraft will create new challenges for visual inspection which still constitutes 80-90% of all inspections. Because surface indicators of damage or delamination may be subtle or barely visible, NASA is interested in technologies and techniques that can enhance visual detectibility in the operational environment. Such innovations could include (but are not limited to) treatments of the composite materials, enhancements to the work environment, or job aids for visual inspectors or maintenance technicians (outside the realm of NDE systems). Desireable features include ease of use and minimal change to the operational process.
  

• Airframe Health Management - including self-awareness and prognosis, anomaly detection and identification, and in-flight damage, degradation and failure mitigation;
  
• Propulsion Health Management - including self-awareness and prognosis of gas path, combustion, and overall engine state (containment systems and rotating and static components), and fault-tolerant system architectures;
  
• Aircraft Systems Health Management - including state-awareness and prognosis of landing gear, hydraulic and pneumatic systems, electrical and power systems, fuel and lubrication systems, avionics/communications, navigation, surveillance/flight critical and flight management systems, and robust, distributed, fault-tolerant, self-recoverable architectures for flight critical aircraft applications;
  
• Environmental Hazard Management - including the prevention, detection, and mitigation of hazards such as ice accretion, lightning strikes, EMI/EMC, and ionizing radiation, as well as the direct and indirect effects of these hazards;
  
• IVHM Architectures and Databases - including system design, analysis and optimization, information management, data flow and communication, control and reconfiguration, architecture development and validation, and database development and management;
  
• Validation and Predictive Capability Assessment - including analysis, simulation, ground testing, flight testing, environmental testing, and software assurance.
  

• Large-scale distributed anomaly, fault, malfunction, degradation, and failure detection with data/decision/information fusion (multiple sensors, actuators, and processing nodes);
  
• Prevention, detection, isolation, and mitigation of multiple independent/correlated anticipated and unanticipated failures (modeling of correlated failures and system/vehicle effects, diagnosis and prognosis, real-time processing and decision-making for very large state spaces, and health state reasoning);
  
• Adaptive diagnostic and prognostic algorithms (adapts as systems and components age, are repaired, or replaced);
  
• Analytical methods to set local decision criteria so that global performance criteria are met (multi-dimensional optimization);
  
• Performance optimization in distributed systems (high probability of detection, low probability of false alarm);
  
• Vehicle-wide state and function monitoring of systems and structures (including digital avionics, auto-flight and control, propulsion, hydraulic, mechanical, pneumatic, electrical, and power generation and distribution systems);
  
• Large-scale distributed adaptive fault-tolerant processing architecture that is robust in adverse operating environments (EMI/EMC, ionizing radiation, low/high temperatures);
  
• Distributed hierarchical threat-tolerant self-healing embedded sensors and systems (embedded self-recovery mechanisms, adaptive, programmable and reconfigurable devices);
  
• Technology integration, verification, and validation (diagnostic and prognostic flight, airframe, and propulsion systems, environmental hazard management, advanced sensors and system architectures, Verification and Validation (V&V) with predictive capability).
  

• Improved structural analysis of complex metallic and composite airframe components through the use of novel multi-scale as well as global-local analytical codes. The methods used for these solutions need to detail the initiation and progression of damage to determine accurate estimates of residual life and or strength of complex airframe structures.
  
• Type II hot corrosion of turbine alloys is a product of molten salt exposure and is manifested by a localized pitting corrosion attack. Prolonged high temperature exposures of turbine disk alloys to sulfur-rich low temperature melting eutectic salts can lead to an onset of Type II hot corrosion attack causing serious degradation to the durability of the turbine components. Tools and models are needed to predict the onset and the rates of hot corrosion attack in these types of alloys.
  
• Simulation of the response to jet engine fan blade-out events of advanced composite fan case/containment structures in aged conditions, using relevant impact mechanics and structural system dynamics modeling techniques.
  

• Inertial navigation units which combine gyroscopic measurements with GPS position data;
  
• The capabilities to implement an autopilot fail-safe should RF uplink be lost;
  
• The ability to log instrumentation data from analog, pulse-width and serial stream inputs;
  
• The ability to read and generate serial-port data streams for RF communication systems;
  
• Telemetry systems to provide for both ground-based piloting and real-time data downlink.
  

• Suppression of all ASE interactions with no a priori knowledge of structural modes;
  
• Minimal interference/interaction with rigid body controller;
  
• Implementable in a real-time flight control processor.
  

• Adaptive filtering techniques;
  
• Self-tuning notch filters;
  
• ASE modeling and predictive techniques;
  
• Online margin measurement techniques;
  
• Online identification of structural vibrations;
  
• Global stability proofs for adaptive systems.
  

• Design tools/decision support systems that account for the needs of IVHM, including sensing, processing, data collection, onboard data mining, and fault diagnostics and prognostics algorithms.
  
• A project database that stores and manages test data, failure statistics, fault modes and effects, diagnostic and prognostic models, simulations, and related documentation for all the systems, subsystems, and components. The IVHM database must also allow for seamless integration with a variety of IVHM algorithms, including data mining, machine learning, and exploratory data analysis tools, in order to enable algorithm development and knowledge discovery using the same database of historical data.
  
• Advances in compositional verification supported by High Confidence Real-Time Operating Systems (RTOS), Middleware (MW), and/or Virtual Machines (VM) that may be independently designed and verified. Desired system properties include dynamic re-allocation of computational resources; correct and consistent disambiguation of fault syndromes, particularly with respect to segregating faults within the computational infrastructure from faults in other vehicle systems; and graceful evolution of system capabilities, with minimum adverse effects due to parts and software obsolescence.
  
• New tools, methods, and processes for verification and validation of diagnostic algorithms with non-deterministic behavior. A desired outcome from this research effort would be a demonstration of the relevance of the tools, methods, and processes towards flight software acceptance as applied to a specific non-deterministic algorithm (e.g., neural network, genetic algorithm, fuzzy rule-based inference, etc.).
  

• Computational approaches to support determining appropriate human-automation function allocations with respect to safety and performance;
  
• Design tools and methods that improve the application of human-centered design principles to the design and certification of mixed human-automated systems;
  
• Tools and methods for modeling the complex information management systems required for future flight deck systems;
  
• Methods of data uncertainty estimation during the flight deck system design phase particularly with respect to overall system integrity;
  
• Design and analysis methods or tools to better predict and assess human and system performance in relevant operational environments;
  
• Tools to extract information from analog information flows and transform to usable information content.
  

• Adaptive mathematical framework for control-centric onboard aircraft models that can accommodate real-time changes to subsystem dynamics;
  
• Real-time system identification capability for updating an onboard vehicle model with adaptive structure to satisfy sub-system constraints under adverse conditions;
  
• Real-time fault diagnostic and prognostics capability needed in adaptive flight, propulsion, structural control application;
  
• Real-time control power map identification with inclusion of aircraft sub-system constraints under adverse conditions;
  
• Real-time dynamic flight envelope identification and prediction capability;
  
• Metrics and assessment models for safety-of-flight diagnostics and prognostics.
  

[back to top]