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At least 199 records · Page 11

Fundamental Technology Development for Gas-Turbine Engine Health Management

Integrated vehicle health management technologies promise to dramatically improve the safety of commercial aircraft by reducing system and component failures as causal and contributing factors in aircraft accidents. To realize this promise, fundamental technology development is needed to produce reliable health management components. These components include diagnostic and prognostic algorithms, physics-based and data-driven lifing and failure models, sensors, and a sensor infrastructure including wireless communications, power scavenging, and electronics. In addition, system assessment methods are needed to effectively prioritize development efforts. Development work is needed throughout the vehicle, but particular challenges are presented by the hot, rotating environment of the propulsion system. This presentation describes current work in the field of health management technologies for propulsion systems for commercial aviation.

Mercer, Carolyn R.↗

Office of Biological and Physical Research: Overview Transitioning to the Vision for Space Exploration

Viewgraphs on NASA's transition to its vision for space exploration is presented. The topics include: 1) Strategic Directives Guiding the Human Support Technology Program; 2) Progressive Capabilities; 3) A Journey to Inspire, Innovate, and Discover; 4) Risk Mitigation Status Technology Readiness Level (TRL) and Countermeasures Readiness Level (CRL); 5) Biological And Physical Research Enterprise Aligning With The Vision For U.S. Space Exploration; 6) Critical Path Roadmap Reference Missions; 7) Rating Risks; 8) Current Critical Path Roadmap (Draft) Rating Risks: Human Health; 9) Current Critical Path Roadmap (Draft) Rating Risks: System Performance/Efficiency; 10) Biological And Physical Research Enterprise Efforts to Align With Vision For U.S. Space Exploration; 11) Aligning with the Vision: Exploration Research Areas of Emphasis; 12) Code U Efforts To Align With The Vision For U.S. Space Exploration; 13) Types of Critical Path Roadmap Risks; and 14) ISS Human Support Systems Research, Development, and Demonstration. A summary discussing the vision for U.S. space exploration is also provided.

Crouch, Roger↗

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include (1) diagnostic/detection methodology, (2) prognosis/lifing methodology, (3) diagnostic/prognosis linkage, (4) experimental validation, and (5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multimechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

Arnold, Steven M.↗

An Overview of Prognosis Health Management Research at Glenn Research Center for Gas Turbine Engine Structures With Special Emphasis on Deformation and Damage Modeling

Herein a general, multimechanism, physics-based viscoelastoplastic model is presented in the context of an integrated diagnosis and prognosis methodology which is proposed for structural health monitoring, with particular applicability to gas turbine engine structures. In this methodology, diagnostics and prognostics will be linked through state awareness variable(s). Key technologies which comprise the proposed integrated approach include (1) diagnostic/detection methodology, (2) prognosis/lifing methodology, (3) diagnostic/prognosis linkage, (4) experimental validation, and (5) material data information management system. A specific prognosis lifing methodology, experimental characterization and validation and data information management are the focal point of current activities being pursued within this integrated approach. The prognostic lifing methodology is based on an advanced multimechanism viscoelastoplastic model which accounts for both stiffness and/or strength reduction damage variables. Methods to characterize both the reversible and irreversible portions of the model are discussed. Once the multiscale model is validated the intent is to link it to appropriate diagnostic methods to provide a full-featured structural health monitoring system.

Arnold, Steven M.↗

High-Fidelity Modeling for Health Monitoring in Honeycomb Sandwich Structures

High-Fidelity Model of the sandwich composite structure with real geometry is reported. The model includes two composite facesheets, honeycomb core, piezoelectric actuator/sensors, adhesive layers, and the impactor. The novel feature of the model is that it includes modeling of the impact and wave propagation in the structure before and after the impact. Results of modeling of the wave propagation, impact, and damage detection in sandwich honeycomb plates using piezoelectric actuator/sensor scheme are reported. The results of the simulations are compared with the experimental results. It is shown that the model is suitable for analysis of the physics of failure due to the impact and for testing structural health monitoring schemes based on guided wave propagation.

Luchinsky, Dimitry G.↗

Uncovering heterogeneous intercommunity disease transmission from neutral allele frequency time series

The COVID-19 pandemic has underscored the need for accurate epidemic forecasting to predict pathogen spread, evolution, and evaluate intervention strategies. Forecast reliability hinges on detailed knowledge of disease transmission across population segments, which may be inferred from contact surveys or mobility data. However, these indirect approaches make it difficult to estimate rare transmissions between socially or geographically distant communities. We show that the steep ramp-up of genome sequencing surveillance during the pandemic can be leveraged to directly identify transmission patterns between geographically defined communities. Our approach uses a hidden Markov model to infer the fraction of infections a community imports from others based on how rapidly allele frequencies in the focal community converge to those in the donor communities. Applying this method to SARS-CoV-2 sequencing data from England and the United States, we uncover networks of intercommunity transmission that reflect geographical relationships while exposing significant long-range interactions. The scaling of importation rate with distance is consistent across both countries, yet weaker than expected based on mobility data, highlighting limitations of indirect inference. We show that transmission patterns can change between waves of variants of concern and analyze how the inferred heterogeneity in intercommunity transmission impacts evolutionary forecasts. While applied here to geographically defined communities, our approach could be applied to those defined by other traits (e.g., age, socioeconomic status), provided time-series data can be stratified accordingly. Overall, our study highlights population genomic time series data as a crucial record of epidemiological interactions, which can be deciphered using tree-free inference methods.

Okada, Takashi [Department of Physics; University ↗

Radiation-Related Risk Analysis for Atmospheric Flight Civil Aviation Flight Personnel

Human data on low dose rate radiation exposure and consequent effects are not readily available, and this fact generates groundtruth concerns for all risk assessment techniques for possible health effects induced by the space radiation environment, especially for long term missions like those foreseen now and in the near future. A large amount of such data may be obtained through civil aviation flight personnel cohorts, in the form of epidemiological studies on delayed health effects induced by the cosmic-ray generated atmospheric radiation environment, a high- LET low dose and low dose rate ionizing radiation with its typical neutron component, to which flight personnel are exposed all throughout their work activity. In the perspective of worldwide studies on radiation exposure of the civil aviation flight personnel, all the available results from previous studies on flight personnel radiation exposure have been examined in various ways (i.e. literature review, meta-analysis) to evaluate possible significant associations between atmospheric ionizing radiation environment and health risks, and to assess directions for future investigations. The physical characteristics of the atmospheric ionizing radiation environment make the results obtained for atmospheric flight personnel relevant for space exploration.

DeAngelis, G.↗

A method for calculating proton-nucleus elastic cross-sections

Recently [Nucl. Instr. and Meth. B 145 (1998) 277; Extraction of in-medium nucleon-nucleon amplitude from experiment, NASA-TP, 1998], we developed a method of extracting nucleon-nucleon (N-N) cross-sections in the medium directly from experiment. The in-medium N-N cross-sections form the basic ingredients of several heavy-ion scattering approaches including the coupled-channel approach developed at the NASA Langley Research Center. We investigated [Proton-nucleus total cross-sections in coupled-channel approach, NASA/TP, 2000; Nucl. Instr. and Meth. B 173-174 (2001) 391] the ratio of real to imaginary part of the two body scattering amplitude in the medium. These ratios are used in combination with the in-medium N-N cross-sections to calculate proton-nucleus elastic cross-sections. The agreement is excellent with the available experimental data. These cross-sections are needed for the radiation risk assessment of space missions. c2002 Elsevier Science B.V. All rights reserved.

NASA Center LaRC↗

Accurate universal parameterization of absorption cross sections III--light systems

Our prior nuclear absorption cross sections model [R.K. Tripathi, F.A. Cucinotta, J.W. Wilson, Nucl. Instr. and Meth. B 117 (1996) 347; R.K. Tripathi, J.W. Wilson, F.A. Cucinotta, Nucl. Instr. and Meth. B 129 (1997) 11] is extended for light systems (A < or = 4) where either both projectile and target are light particles or one is light particle and the other is medium or heavy nucleus. The agreement with experiment is excellent for these cases as well. Present work in combination with our original model provides a comprehensive picture of absorption cross sections for light, medium and heavy systems. As a result the extended model can reliably be used in all studies where there is a need for absorption cross sections.

NASA Discipline Radiation Health↗

Visual-motor recalibration in geographical slant perception

In 4 experiments, it was shown that hills appear steeper to people who are encumbered by wearing a heavy backpack (Experiment 1), are fatigued (Experiment 2), are of low physical fitness (Experiment 3), or are elderly and/or in declining health (Experiment 4). Visually guided actions are unaffected by these manipulations of physiological potential. Although dissociable, the awareness and action systems were also shown to be interconnected. Recalibration of the transformation relating awareness and actions was found to occur over long-term changes in physiological potential (fitness level, age, and health) but not with transitory changes (fatigue and load). Findings are discussed in terms of a time-dependent coordination between the separate systems that control explicit visual awareness and visually guided action.

NASA Center ARC↗

Model Based Approaches for Fault Detection, Prognostics, Decision Making in Complex Systems

The presentation discusses application of model based approaches to complex systems. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

Health and Safety Effects of Airborne Soil Dust in the Americas and Beyond

Risks associated with dust hazards are often underappreciated, a gap between the knowledge pool and public awareness that can be costly for impacted communities. This study reviews the emission sources and chemical, physical, and biological characteristics of airborne soil particles (dust) and their effects on human and environmental health and safety in the Pan-American region. American dust originates from both local sources (western United States, northern Mexico, Peru, Bolivia, Chile, and Argentina) and long-range transport from Africa and Asia. Dust properties, as well as the trends and interactions with criteria air pollutants, are summarized. Human exposure to dust is associated with adverse health effects, including asthma, allergies, fungal infections, and premature death. In the Americas, a well-documented and striking effect of soil dust is its association with Coccidioidomycosis, commonly known as Valley fever, an infection caused by inhalation of soil-dwelling fungi unique to this region. Besides human health, dust affects environmental health through nutrients that increase phytoplankton biomass, contaminants that diminish water supply and affect food (crops/fruits/vegetables and ready-to-eat meat), spread crop and marine pathogens, cause Valley fever among domestic and wild animals, transport heavy metals, radionuclides and microplastics, and reduce solar and wind power generation. Dust is also a safety hazard to road transportation and aviation, in the southwestern US where blowing dust is one of the deadliest weather hazards. To mitigate the harmful effects, coordinated regional and international efforts are needed to enhance dust observations and prediction capabilities, soil conservation measures, and Valley fever and other disease surveillance.

health↗

Human System Risk Management for Space Flight

This brief abstract reviews the development of the current day approach to human system risk management for space flight and the development of the critical components of this process over the past few years. The human system risk management process now provides a comprehensive assessment of each human system risk by design reference mission (DRM) and is evaluated not only for mission success but also for long‐term health impacts for the astronauts. The discipline of bioastronautics is the study of the biological and medical effects of space flight on humans. In 1997, the Space Life Sciences Directorate (SLSD) initiated the Bioastronautics Roadmap (Roadmap) as the "Critical Path Roadmap", and in 1998 participation in the roadmap was expanded to include the National Space Biomedical Research Institute (NSBRI) and the external community. A total of 55 risks and 250 questions were identified and prioritized and in 2000, the Roadmap was base-lined and put under configuration control. The Roadmap took into account several major advisory committee reviews including the Institute of Medicine (IOM) "Safe Passage: Astronaut care for Exploration Missions", 2001. Subsequently, three collaborating organizations at NASA HQ (Chief Health and Medical Officer, Office of Space Flight and Office of Biological & Physical Research), published the Bioastronautics Strategy in 2003, that identified the human as a "critical subsystem of space flight" and noted that "tolerance limits and safe operating bands must be established" to enable human space flight. These offices also requested a review by the IOM of the Roadmap and that review was published in October 2005 as "A Risk Reduction Strategy for Human Exploration of Space: A Review of NASA's Bioastronautics Roadmap", that noted several strengths and weaknesses of the Roadmap and made several recommendations. In parallel with the development of the Roadmap, the Office of the Chief Health and Medical Officer (OCHMO) began a process in 2004 of evaluating the tolerance limits and safe operating bands called for in the Bioastronautics Strategy. Over the next several years, the concept of the "operating bands" were turned into Space Flight Human System Standards (SFHSS), developed by the technical resources of the SLSD at the NASA Johnson Space Center (JSC). These standards were developed and reviewed at the SLSD and then presented to the OCHMO for acceptance. The first set of standards was published in 2007 as the NASA‐STD‐3001, Volume 1, Crew Health that elaborated standards for several physiological areas such as cardiovascular, musculoskeletal, radiation exposure and nutrition. Volume 2, Human Factors, Habitability and Human Health was published in 2011, along with development guidance in the Human Integration Design Handbook (HIDH). Taken together, the SFHSS Volumes 1 and 2, and the HIDH replaced the NASA‐STD‐3000 with new standards and revisions of the older document. Three other changes were also taking place that facilitated the development of the human system risk management approach. In 2005, the life sciences research and development portfolio underwent a comprehensive review through the Exploration Systems Architecture Study (ESAS) that resulted in the reformulation of the Bioastronautics Program into Human Research Program (HRP) that was focused on appropriate mitigation results for high priority human health risks. The baseline HRP budget was established in August 2005. In addition, the OCHMO formulated the Health and Medical Technical Authority (HMTA) in 2006 that established the position of the Chief Medical Officer (CMO) at the NASA JSC along with other key technical disciplines, and the OCHMO became the responsible office for the SFHSS as noted above. The final change was the establishment in 2008 of the Human System Risk Board (HSRB), chaired by the CMO with representation from the HRP, SLSD management and technical experts. The HSRB then began to review all human system risks, established a comprehensive risk management and configuration management plan and data sharing policy. These major developments of standards, the HRP, the HMTA and a forum for review of human system risks (HSRB) facilitated the integration of human research, medical operations, systems engineering and many other disciplines in the comprehensive review of human system risks. The HSRB began a comprehensive review of all potential inflight medical conditions and events and over the course of several reviews consolidated the number of human system risks to 30 where the greatest emphasis is placed for investing program dollars for risk mitigation. The HSRB considers all available evidence from human research, medical operations and occupational surveillance in assessing the risks for appropriate mitigation and future work. All applicable DRMs (low earth orbit 6 and 12 months, deep space sortie for 30 days and 1 year, a one year lunar mission, and a planetary mission for 3 years) are considered as human system risks are modified by the hazards associated with space flight such as microgravity, exposure to radiation, distance from the earth, isolation and a closed environment. Each risk has a summary assessment representing the state of knowledge/evidence base for that risk, the available risk mitigations, traceability to the SFHSS and program requirements, and future work required. These data then can drive coordinated budgets across the HRP, the International Space Station, Crew Health and Safety and Advanced Exploration System budgets. These risk assessments were completed for 6 DRMs in December of 2014 and serve as the baseline for which subsequent research and technology development and crew health care portfolios can be assessed. The HSRB will review each risk at least annually and especially when new information is available that must be considered for effective risk mitigation. The current status of each risk can be reported to program management for operations, budget reviews and general oversight of the human system risk management program.

Davis, Jeffrey↗