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NASA NTRSDate not supplied

An Approach for Defining IASMS Services, Functions, and Capabilities

Assuring safety in the NAS with the inclusion of new entrants, such as Advanced Air Mobility (AAM), will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using autonomous vehicles. The focus of the In-time Aviation Safety Management System (IASMS) is to overcome AAM’s safety assurance challenges. The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs) designed to manage operational risks, identify unknown risks, and inform system designs. This paper describes an approach for defining SFCs based on technology trends in research, assessment of known and unknown risks in voluntary safety reports, and causal and contributing factors in aviation accidents and incidents. This approach would identify potential SFCs that further expand the Monitor, Assess, and Mitigate (M-A-M) functionality that represents the enabling framework of the IASMS. Safety implications that will result from integration of AAM in the transformation of the National Airspace System (NAS) were addressed in National Academies committees reports on AAM and IASMS. Development of a ConOps for IASMS was a top recommendation and can be represented as a reframing of safety assurance that builds on real-time alerting such as the Traffic Alert and Collision Avoidance System, and adds the more encompassing in-time temporal parameter in recognition of the different timelines for collecting and assessing safety data for risk mitigations. For example, mining for safety trends from data sources such as the Aviation Safety Information Analysis and Sharing system occurs over a longer time period. Research on AAM operations poses that SFCs can be designed to monitor the safety margin appropriate for AAM including with regards to the distance between current flight parameters and nominal ideal conditions. These in-time comparisons will become more complex as the density of operations increases at least in certain areas and can include planned and actual 4D trajectory, and in-time comparisons having implications on conflict modeling and prediction including expected and actual departure time, fix/waypoint crossing times, and arrival time. These comparisons would be integrated as part of SFCs that redefine and inform new safety margin. An increased safety margin improves management of operational risks while reducing the potential for anomalies. An increased safety margin also has implications for operator confidence in the certainty of its operations and trust in automation. Technology trends in research could be used to refine existing SFCs and define needs for additional SFCs that provide safety improvements to the design and operation of vehicles, airspace design, and operator performance requirements. NASA is developing innovative approaches to safeguard against major accidents and incidents that have occurred in the NAS and those anticipated with the inclusion of envisioned AAM operations. The innovations use operational performance data to monitor, detect, and predict flight variations exceeding safe nominal patterns, such as would be caused by navigational error, severe weather complications, or hijacking of UAS controls. These innovative approaches have high potential to prevent accidents and incidents in the new AAM era. It is anticipated that elements of the innovations will evolve into SFCs for the IASMS. Voluntary safety reports can be monitored to identify anomalies related to design or operational performance risks. Reports could be periodically monitored and assessed for specific topics. Reports might serve as weak signals or precursors indicative of emergent risk such as when combined with other safety information. The architecture could include SFCs that are based on voluntary safety reports recognizing the periodic temporal nature of data analysis. As previously mentioned, aviation accidents with their causal and contributing precursors can inform the need for SFCs in the IASMS. Accidents and incidents at San Francisco International Airport such as Asiana 214 and Air Canada 759 illustrate how combinations of different factors lead to increased risk. These types of precursors and different factors have implications on the types of SFCs that could be needed to monitor and manage different sources and types of design and operational risk. Continuing to assure the safety of AAM as designs and operations gain in complexity can be accompanied by defining SFCs that also increase in complexity. These SFCs can leverage information from findings and recommendations synthesized across on-going research, voluntary safety reports, and accident and incident reports. These SFCs can serve to refine accuracy of algorithms and resolve limitations with current practices. The IASMS architecture represents the framework for the SFCs and their critical role in safety assurance.

In-Time Aviation Safety Management System↗

NASA NTRS2020

Inter-species molecular attraction effect in the development of a two-species mixing layer

Simulations of fluid mixing occurring at high-pressures mandate the use of real-fluid equations of state to capture the effect of boh intermolecular repulsive forces and attractive forces which are neglected in perfect gases. Dedicated mixing rules account for these forces by providing a generic mapping of the mixture state-space onto a pure fluid domain. However, the specific inter-species molecular forces depend on the particular characteristics of the molecular pairs under consideration, and typically require the knowledge of binary interaction coefficients k_{αβ}. The impact of k_{αβ} on a flow field is as of now unclear; further, k_{αβ} is unknown for many molecular pairs and when unknown, set to be null. This study addresses the impact of k_{αβ} on the temporal evolution of a real-fluid mixing layer and uses Direct Numerical Simulation (DNS) to explore this impact. The results show differences between augmented attraction (k_{αβ}<0) and diminished attraction (k_{αβ}>0), affecting both fluid dynamics and diffusion processes. Specifically, in a binary mixing layer, it is observed that augmented attraction leads to delayed transition and mixing layer growth, manifested in the momentum layer thickness. The fractal dimension, which is a manifestation of interface corrugation, is calculated. These results provide an hitherto undocumented mechanism affecting the development of mixing layers.

Banuti, Daniel↗

NASA NTRSDate not supplied

Going beyond reliability to achieve robustness

Reliability is the ability to perform well and consistently. More formally, reliability is defined as the mathematical probability that a system does not fail during a specified time period under its specified operating conditions. The specified operating conditions often go beyond the nominal environment to include variations and challenges encountered in operational use. The difficulty is that systems are often operated outside of their specified operating conditions and, if they fail, the designers are in theory blameless. Unanticipated damaging events include internal failures, external disruptions in supporting systems, accidents, and repurposing. The most common explanation of a system failure is human error, which is usually the first assumption of the system designers. Robustness is the capability to perform without failure under a wide range of conditions that go beyond the specified operating conditions. The first step towards improving robustness would be to expand the system’s specified operating conditions to include a wider range of anticipated challenges, especially human error. Beyond this, there is a need for general approach to reduce the impact of unanticipated future events, the unknown unknowns, by improving the system’s general ability to cope. Robustness can be improved by providing additional processing capacity, larger flow control buffers, increased backup storage, more online redundancy, and more capable supervisory monitoring and control.

Harry W Jones↗

NASA NTRS2019

Exploring Oxidation in the Remote Free Troposphere: Insights From Atmospheric Tomography (ATom)

Earth's atmosphere oxidizes the greenhouse gas methane and other gases, thus determining their lifetimes and oxidation products. Much of this oxidation occurs in the remote, relatively clean free troposphere above the planetary boundary layer, where the oxidation chemistry is thought to be much simpler and better understood than it is in urban regions or forests. The NASA airborne Atmospheric Tomography study (ATom) was designed to produce cross sections of the detailed atmospheric composition in the remote atmosphere over the Pacific and Atlantic Oceans during four seasons. As part of the extensive ATom data set, measurements of the atmosphere's primary oxidant, hydroxyl (OH), and hydroperoxyl (HO2) are compared to a photochemical box model to test the oxidation chemistry. Generally, observed and modeled median OH and HO2 agree to within combined uncertainties at the 2σ confidence level, which is ~±40%. For some seasons, this agreement is within ~±20% below 6-km altitude. While this test finds no significant differences, OH observations increasingly exceeded modeled values at altitudes above 8 km, becoming ~35% greater, which is near the combined uncertainties. Measurement uncertainty and possible unknown measurement errors complicate tests for unknown chemistry or incorrect reaction rate coefficients that would substantially affect the OH and HO2 abundances. Future analysis of detailed comparisons may yield additional discrepancies that are masked in the median values.

atmosphere's primary oxidant, hydroxyl (OH), and h↗

NASA NTRS2021

Seawater Debye Model Function at L-Band and Its Impact on Salinity Retrieval From Aquarius Satellite Data

A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ( S ) and temperature ( T ). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in S and T to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in S and T based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared with in situ data collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models.

Seawater↗

NASA NTRS2021

Atacama Cosmology Telescope: Detection of Millimeter-Wave Transient Sources

We report on the serendipitous discovery of three transient millimeter-wave sources using data from the Atacama Cosmology Telescope. The first, detected at R.A. = 273.8138, decl. = −49.4628 at ∼50σ total, brightened from less than 5 mJy to at least 1100 mJy at 150 GHz with an unknown rise time shorter than 13 days, during which the increase from 250 mJy to 1100 mJy took only 8 minutes. Maximum flux was observed on 2019 November 8. The source's spectral index in flux between 90–150 GHz was positive, α = 1.5 ± 0.2. The second, detected at R.A. = 105.1584, decl. = −11.2434 at ∼20σ total, brightened from less than 20 mJy to at least 300 mJy at 150 GHz with an unknown rise time shorter than 8 days. Maximum flux was observed on 2019 December 15. Its spectral index was also positive, α = 1.8 ± 0.2. The third, detected at R.A. = 301.9952, decl. = 16.1652 at ∼40σ total, brightened from less than 8 mJy to at least 300 mJy at 150 GHz over a day or less but decayed over a few days. Maximum flux was observed on 2018 September 11. Its spectrum was approximately flat, with a spectral index of α = −0.2 ± 0.1. None of the sources were polarized to the limits of these measurements. The two rising-spectrum sources are coincident in position with M and K stars, while the third is coincident with a G star.

Time domain astronomy↗

NASA NTRSDate not supplied

An Approach for Identifying IASMS Services, Functions, and Capabilities From Data Sources

Assuring safety in the NAS with the inclusion of new entrants that are part of Advanced Air Mobility (AAM) will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using semi-autonomous/autonomous vehicles. Overcoming these AAM safety assurance challenges is the focus of the In-time Aviation Safety Management System (IASMS). The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs)designed to manage operational risks, identify unknown risks, and inform system design to mitigate risk. This paper describes a broad approach for identifying SFCs involving technology trends in research, assessment of known and unknown risks in safety reports, and causal and contributing factors in aviation accidents and incidents. This approach leverages these sources to identify potential SFCs that enable the Monitor, Assess, and Mitigate (M-A-M)functionality that represents the enabling framework of the IASMS.

Kyle Ellis↗

NASA NTRS2021

Space Environmental Effects on Microbial Growth and Survival

As humanity moves from earth to the moon and beyond, there are many challenges, both known and unknown, which will complicate our path. One unknown variable is how microorganisms will be affected by the space environment, and how this may impact mission success. There is a serious need for greater research to be conducted on how space environments alter microbial organisms genetically and physiologically, and to understand the threats that potential microbial changes may present to future missions.

Chelsi D. Cassilly↗

NASA NTRS2020

Using Flexible Execution, Replanning, and Model Parameter Updates to Address Environmental Uncertainty for a Planetary Lander

When planning in unknown environments, a planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution. Productivity in the face of these challenges requires an integrated approach to planning and execution that is capable of reacting to variation while still maximizing overall utility. We examine this problem in the context of the Europa Lander mission concept. We propose a planning and execution framework that responds to feedback using three techniques: (1) flexible execution, (2) periodic replanning, and (3) online model parameter and utility updates. The efficacy of each of these techniques is examined through simulation of the Europa Lander concept, showing higher utility achievement compared to baseline approaches.

Chien, Steve A.↗

NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Traditional risk-based design processes seek to mitigate operational hazards by manually identifying possible faults and corresponding mitigation strategies—a tedious process which critically relies on the designer’s limited knowledge. Resilience-based design, on the other hand, seeks to embody generic hazard-mitigating properties in the system to mitigate unknown hazards, often by modelling the system's response to potential hazardous events. This work adapts this approach to the traditional risk-based design process to synthetically generate hazardous modes, by representing them as a unique combination of internal component health-states which can then be injected and simulated in a model of the system failure dynamics. The design process may then reduce the risk of unknown internal hazards by iteratively mitigating the effects of these modes. The performance of this approach is evaluated in a model of an autonomous rover, where cluster analysis shows that elaborating the space of synthetic faults in the drive system using this approach uncovers a wider range of possible hazardous trajectories and failure consequences within each trajectory. However, this increase in hazard information comes at a high computational expense, highlighting the need for advanced, efficient methods to search and sample the hazard space.

Simulation↗

NASA NTRSDate not supplied

Starfleet Medical: Knowledge Skills and Abilities of Crew Medical Officers on Exploration Missions

Anticipating the medical skills necessary for deep space exploration missions is one of the greatest space medicine challenges. Spontaneous medical events unrelated to the space environment for which resourcing is severely limited. Novel medical events in the space environment for which are unknown unknowns. Exploration missions are remote, long duration, and low resource which increases the risk and consequences of medical events and require increased crew autonomy.

Levin, DR↗

NASA NTRSDate not supplied

Synthetic Failure Mode Generation for Resilience Analysis and Failure Mechanism Discovery

Simulation↗

NASA NTRSDate not supplied

Developing Deep Learning Models for System Remaining Useful Life Predictions: Application to Aircraft Engines

Prognostics and health management (PHM) is an important part of ensuring reliable operations of complex safety- critical systems. System-level remaining useful life (RUL) estimation is a much more complex problem than making estimations at the component level, and system-level RUL methodologies remain sparse in the literature. Model-based approaches have traditionally worked in the past for components such as capacitors, MOSFETs, batteries, or hard-drives (to name a few examples), but developing high fidelity dynamics models of cyber physical systems that can be used to study the effects of multiple degrading components in the system remains a challenging task. Some initial work on model-based System RUL predictions was demonstrated in Khorasgani, et al [1], but, to generalize the system-level prognostics problem, we have to resort to pure data driven and hybrid approaches. In this work, we propose an end-to-end data- driven framework for developing deep learning models to predict remaining useful life of cyber physical systems operating under unknown faulty conditions. The raw data is organized with a data schema that improves the model development process and down stream data analysis tasks. Due to the unknown faulty conditions, the raw sensor data is transformed into signals that expose the underlying degradation processes, which are then used for model development. Bayesian Optimization is used to tune the model parameters prior to training and validation. We show that this approach results in accurate predictions within 3 cycles to end of life (EOL). We demonstrate the effectiveness of our approach by applying it to the N-CMAPSS turbofan engine dataset recently released by NASA, which includes high fidelity degradation modeling, real world operating conditions, and a large set of fault operating modes.

Prognostics↗

NASA NTRS2022

Requirements for Progress in Understanding Solar Flare Energy Transport: The Gradual Phase

Solar flares are a fundamental component of solar eruptive events (SEEs), along with solar energetic particles (SEPs) and coronal mass ejections (CMEs). Flare emission is the first component of a SEE to impact the Earth’s atmosphere which can set the stage for the later arrival of the associated SEPs, CME, and space weather event. Magnetic reconnection drives SEEs by restructuring the solar coronal magnetic field, liberating a tremendous amount of energy which is partitioned into various physical manifestations: particle acceleration, mass and magnetic-field eruption, atmospheric heating, and the subsequent emission of radiation as solar flares. In this white paper we discuss the observational and theoretical advances required in order to make substantial progress in understanding the physical processes acting during the gradual phase of a flare. That is, the decay period, following the initial rapid release of energy during the impulsive phase (see our other white paper). In particular we want to address the unknown processes that sustain the long decay phase of flares and identify the unknown mechanism and magnitude of continued energy injection during the gradual phase.

Graham S Kerr↗

NASA NTRS2021

A sampling-based optimization approach to handling environmental uncertainty for a planetary lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Zilberstein, Shlomo↗

NASA NTRSDate not supplied

Dust Characterization Needs for Dust Mitigation

Unknowns remain regarding several lunar dust characteristics, and the lunar dust mitigation community, in collaboration with the science community, is compiling a list of lunar dust characterization needs. It is expected, through the course of currently planned or future lunar surface mission instrumentation, lab experiments, or analysis, that some of these unknowns will be characterized. This list is derived from several sources including findings from the 2020 publication “The Impact of Lunar Dust on Human Exploration”, NASA-STD-1008, SLS-SPEC-159 DSNE, and known gaps identified within NASA programs and projects. The goal of this information collection process is to create a list of dust characterization needs based on scientific and engineering knowledge gaps that will aid in the design and survival of designed and future systems, and address the challenges related to defining the lunar environment and mitigating lunar dust effects on systems and operations. Examples of dust properties and characterization needs include detailed examination of the finest fraction of lunar regolith as it varies by surface location, understanding dust transfer within natural and induced environments, determination of the effects of the lunar plasma environment with regards to materials and hardware, understand the triboelectric charging effects for activities and hardware in contact with the regolith, including grain to grain electrostatic interactions, and material adhesive properties.

dust mitigation↗

NASA NTRS2022

Adaptive Force Control for Small Celestial Body Sampling

An adaptive force control algorithm for small celestial body sampling for a variety of surface properties is developed. The control algorithm consists of an adaptive controller combined with feedback linearization. When a spacecraft makes contact with the surface, it must maintain a desired contact force in order to capture a sample. The properties of the surface are unknown or uncertain before contact with the surface is made. The adaptive controller performs system identification online to create an input-output model of the feedback linearized system. From the input-output model a block observable canonical form is realized and the control input is determined by model predictive control (MPC) to maintain a desired contact force in spite of the unknown surface properties. The approach is applied to a variety of surface properties with linear and nonlinear contact models.

Quadrelli, Marco M↗

NASA NTRS2021

A Sampling-Based Optimization Approach to Handling Environmental Uncertainty for a Planetary Lander

Chien, Steve↗