Results for “evaluation framework” — Page 17

NASA NTRS2026

Final Report Document: Microgravity Medical Eyewash B

Senior Capstone Design Team 15 has been commissioned by the National Aeronautics and Space Administration (NASA) to redesign the current microgravity eye wash station. The current design exhibits four primary limitations: single-use operation, excessive mass and volume, operational complexity requiring coordination with external systems, and dependence on frequent Earth resupply missions. Our updated eyewash design will be deployed on long-range space missions that could last multiple years. Based on these and other requirements from our customer, we have developed our updated eyewash design. Our compact, single-eye system uses capillary-driven flow and a free-floating water ball. This design allows us to eliminate pumps and bulky tubing, decreasing the mass and volume of the overall system. The containment eyecup is held to the astronaut's face with a strap, and a silicone seal prevents leakage while improving user comfort. Contaminated water is contained within a disposal bag containing wicking material to pull it back out of the eyecup after washing the eye. This system is reusable, efficient, and easy to activate quickly. We evaluated our design through calculations, microfluidic testing, and user try-on testing to verify our requirements. Our work demonstrated that steady-state flow was capable with our capillary framework, achieving 1.31 L/min. The prototype construction confirmed mass and volume reductions, and try-on-testing evaluated the ability to put on the system quickly. Almost all major design requirements were achieved, and water loss could be validated by testing in microgravity. Our design went through a number of iterations to reach the final solution presented in this report. These changes were driven by our testing and collaboration from each member of the team. Updated models optimized tube placement and geometry of the eyecup to better direct flow as it pools across the eye. Changes to our tubing bends improved capillary efficiency and increased the flow rate we were capable of achieving. And improved bracket placements adjusted the fit, comfort, and seal of the eyecup to the astronaut's face. Our capillary-based eyewash system is technically feasible and has been theoretically validated to operate effectively in microgravity conditions. Testing and analysis indicate that it can meet or exceed the required performance metrics, including flow rate and safety constraints. Overall, the design represents a viable alternative to current ISS-dependent eyewash systems for future space missions.

Jeffrey Allen↗

NASA NTRS1984

Advanced AFCS developments on the XV-15 tilt rotor research aircraft

The design criteria and control and handling qualities of the Automatic Flight Control System (AFCS), developed in the framework of the XV-15 tilt-rotor research aircraft, are evaluated, differentiating between the stability and control criteria. A technically aggressive SCAS control law was implemented, demonstrating that significant benefits accrue when stability criteria are separated from design criteria; the design analyses for application of the control law are presented, and the limit bandwidth for stabilization in hovering flight is shown to be defined by rotor or control lag functions. Flight tests of the aircraft resulted in a rating of 3 on the Cooper-Harper scale; a possibility of achieving a rating of 2 is expected if the system is applied to the yaw and heave control modes.

Churchill, G. B.↗

NASA NTRS1990

Neural network classification - A Bayesian interpretation

The relationship between minimizing a mean squared error and finding the optimal Bayesian classifier is reviewed. This provides a theoretical interpretation for the process by which neural networks are used in classification. A number of confidence measures are proposed to evaluate the performance of the neural network classifier within a statistical framework.

Wan, Eric A.↗

NASA NTRS1993

SEI science - Measuring the return

The ways of plausibly evaluating the scientific payoff of prospective mission architectures within the administrative framework of NASA's Space Exploration Initiative (SEI) are discussed. Attention is given to the expanding goals of a modestly implemented unmanned exploration focus, an aggressive implementation of that focus, and an equally aggressive implementation of a manned exploration priority, relative to the Apollo moon-landing program of the 1960s-1970s.

Budden, Nancy A.↗

NASA NTRS1996

Practical Aspects of Krylov Subspace Iterative Methods in CFD

Implementation issues associated with the application of Krylov subspace iterative methods, such as Newton-GMRES, are presented within the framework of practical computational fluid dynamic (CFD) applications. This paper categorizes, evaluates, and contrasts the major ingredients (function evaluations, matrix-vector products, and preconditioners) of Newton-GMRES Krylov subspace methods in terms of their effect on the local linear and global nonlinear convergence, memory requirements, and accuracy. The discussion focuses on Newton-GMRES in both a structured multi-zone incompressible Navier-Stokes solver and an unstructured mesh finite-volume Navier-Stokes solver. Approximate versus exact matrix-vector products, effective preconditioners, and other pertinent issues are addressed.

Pulliam, Thomas H.↗

NASA NTRS2017

Real-Time Risk Assessment Framework for Unmanned Aircraft System (UAS) Traffic Management (UTM)

The new Federal Aviation Administration (FAA) Small Unmanned Aircraft rule (Part 107) marks the first national regulations for commercial operation of small unmanned aircraft systems (sUAS) under 55 pounds within the National Airspace System (NAS). Although sUAS flights may not be performed beyond visual line-of-sight or over non- participant structures and people, safety of sUAS operations must still be maintained and tracked at all times. Moreover, future safety-critical operation of sUAS (e.g., for package delivery) are already being conceived and tested. NASA's Unmanned Aircraft System Trac Management (UTM) concept aims to facilitate the safe use of low-altitude airspace for sUAS operations. This paper introduces the UTM Risk Assessment Framework (URAF) which was developed to provide real-time safety evaluation and tracking capability within the UTM concept. The URAF uses Bayesian Belief Networks (BBNs) to propagate off -nominal condition probabilities based on real-time component failure indicators. This information is then used to assess the risk to people on the ground by calculating the potential impact area and the effects of the impact. The visual representation of the expected area of impact and the nominal risk level can assist operators and controllers with dynamic trajectory planning and execution. The URAF was applied to a case study to illustrate the concept.

Ancel, Ersin↗

NASA NTRS2020

Dust Aerosol Retrieval Over the Oceans With the MODIS/VIIRS Dark Target Algorithm: 2. Nonspherical Dust Model

The Dark‐target (DT) aerosol algorithm retrieves spectral Aerosol Optical Depth (AOD) and other aerosol properties from Moderate‐resolution Imaging Spectrometer (MODIS) reflectance observations. Over the ocean, the DT algorithm is known to contain scattering‐angle‐dependent biases in its retrievals of AOD, Angstrom Exponent (AE), and Fine Mode Fraction (FMF) for dust aerosols. Following a two‐step strategy to improve the DT retrieval of dust over ocean, for which the first step is to identify dusty pixels (reported in “Part 1”), in this “Part 2,” we report on construction of a new dust model lookup table (LUT) and the strategy for applying it within the existing DT algorithm. In particular, we evaluate different characterizations of dust optical properties from a variety of frameworks and databases, and compare them with the current DT retrieval assumptions. Substituting the standard operational LUT with a spheroid dust model with identified dusty pixels shows significant improvement when compared with collocated AERONET‐identified dusty pixels. Specifically, the application of the new dust model to dusty pixels reduces their AOD bias from 0.06 to 0.02 while improving the fraction of retrievals within expected error from 64% to 82%. At the same time, the overall bias in AE is reduced from 0.13 to 0.06, and the scattering‐angle‐dependent AE bias is largely eliminated. In testing on two full months of data (April and July), the new retrieval will reduce the monthly mean AOD by up to 0.1 and 0.2 in the north Atlantic and Arabian seas, respectively. The average AE and FMF are also reduced in these dust heavy regions

dust aerosol retrieval↗

NASA NTRSDate not supplied

Conceptual Exploration of Aircraft Configurations for the SUSAN Electrofan

Summary of Work - Explored the aircraft configuration trade space for NASA’s Subsonic Single Aft Engine (SUSAN) Electrofan concept – a 180 passenger regional jet aircraft that features advanced electrified aircraft propulsion (EAP) and propulsion-airframe integration (PAI) technologies - Evaluated concepts through a low-order multidisciplinary design and analysis framework

Timothy Chau↗

NASA NTRS2016

Initial Demonstration of the Real-Time Safety Monitoring Framework for the National Airspace System Using Flight Data

As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper we demonstrate the framework's capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the capability of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.

Real-time Safety Monitoring↗

NASA NTRSDate not supplied

A Distributed Simulation-to-Flight Framework to Support Investigating Trust/Trustworthiness in Multi-Agent Systems

As autonomous systems continue to grow both in use and complexity, the necessity for robust and extensible simulation-to-flight frameworks is paramount for establishing an effective architecture for autonomous systems. Hardware test flights are time-consuming and cost prohibitive during early system design and development. Simulation environments can be useful tools to accelerate algorithm development and testing. However, transitions from simulation to flight (sim-to-flight) can be challenging, unless systems are designed with this transition in mind and with the necessary capabilities built into the architecture and framework. One of the objectives of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR) was to design and develop a distributed mixed-reality simulation environment to begin establishing a basis for certification of autonomous systems via research into trust and trustworthiness. ATTRACTOR’s objective was to construct computational concepts of trustworthiness and justifiable trust in multi-agent autonomous teams, to inform future certification of safety-critical and time-critical autonomous systems in aviation. In this paper, we present an autonomous systems architecture and development framework paired with a persistent distributed modeling and simulation (ModSim) environment for test and evaluation of autonomous systems. They were designed under ATTRACTOR in order to measure and establish trustworthiness and trust in single-and multi-agent human-machine systems whether these machines are fixed-wing general aviation, rotary-wing Unmanned Aerial Vehicles (UAVs), ground rovers, or even spacecraft. The Autonomous Entity Operational Network (AEON) framework enables autonomous system development with an easily extensible collection of libraries and plug-n-play nodes facilitated by the Data Distribution Service (DDS) communication protocol standard. The Baseline Environment for Autonomous Modeling (BEAM) simulation environment is a distributed mixed-reality Unity™-based environment built around the same DDS communication paradigm allowing for easy integration with AEON-based autonomous applications, enabling sim-to-flight with minimal configuration changes. Using AEON and BEAM, source code that runs in simulation ports directly to hardware and has successfully flown in the National Airspace System (NAS) at NASA LaRC many times over the lifetime of ATTRACTOR.

Benjamin N Kelley↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRSDate not supplied

A Prognostics Framework for Battery Health Monitoring Integrated with Thermal Modeling

Urban Air Mobility (UAM) promises to revolutionize transportation in major cities, offering passenger travel, cargo delivery, and emergency medical services through a network of electric vertical takeoff and landing (eVTOL) aircraft. However, the limited range of current eVTOLs, due to the low specific energy of lithium-ion batteries along with a possibility of thermal runaway conditions poses significant safety concerns, leading to potentially compromising operational safety. To address this critical challenge, researchers are actively evaluating the impact of flight and environmental conditions on onboard lithium-ion battery health. This involves carefully assessing the performance of battery packs under laboratory and operational conditions for developing models to estimate future health using prognostics framework. This study examines the effectiveness of evaluating battery degradation leading to catastrophic failures under varying operational conditions in laboratory. These are captured using physics based models of underlying phenomenons and integrated into the prognostics framework. A fully charged battery undergoes controlled discharge cycles at varying C-rates based on the simulated power draw profile, with current and voltage, temperature data recorded throughout the experiment. The observed data provides valuable insights into how different operating conditions and mission profiles affect battery performance. This information is crucial for developing strategies to optimize battery systems, enhance range, and ultimately ensure the safe and reliable operation of UAM vehicles.

Thermal Modeling↗

NASA NTRS2013

Technology Estimating: A Process to Determine the Cost and Schedule of Space Technology Research and Development

NASA is investing in new technologies that include 14 primary technology roadmap areas, and aeronautics. Understanding the cost for research and development of these technologies and the time it takes to increase the maturity of the technology is important to the support of the ongoing and future NASA missions. Overall, technology estimating may help provide guidance to technology investment strategies to help improve evaluation of technology affordability, and aid in decision support. The research provides a summary of the framework development of a Technology Estimating process where four technology roadmap areas were selected to be studied. The framework includes definition of terms, discussion for narrowing the focus from 14 NASA Technology Roadmap areas to four, and further refinement to include technologies, TRL range of 2 to 6. Included in this paper is a discussion to address the evaluation of 20 unique technology parameters that were initially identified, evaluated and then subsequently reduced for use in characterizing these technologies. A discussion of data acquisition effort and criteria established for data quality are provided. The findings obtained during the research included gaps identified, and a description of a spreadsheet-based estimating tool initiated as a part of the Technology Estimating process.

Cole, Stuart K.↗

NASA NTRS1989

Maintenance = reuse-oriented software development

Maintenance is viewed as a reuse process. In this context, a set of models that can be used to support the maintenance process is discussed. A high level reuse framework is presented that characterizes the object of reuse, the process for adapting that object for its target application, and the reused object within its target application. Based upon this framework, a qualitative comparison is offered of the three maintenance process models with regard to their strengths and weaknesses and the circumstances in which they are appropriate. To provide a more systematic, quantitative approach for evaluating the appropriateness of the particular maintenance model, a measurement scheme is provided, based upon the reuse framework, in the form of an organized set of questions that need to be answered. To support the reuse perspective, a set of reuse enablers are discussed.

Basili, Victor R.↗

NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

climate projection↗

NASA NTRS2016

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km by 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

general circulation model (GCM)↗

NASA NTRS2017

A Human-Autonomy Teaming Approach for a Flight-Following Task

Human involvement with increasingly autonomous systems must adjust to allow for a more dynamic relationship involving cooperation and teamwork. As part of an ongoing project to develop a framework for human autonomy teaming (HAT) in aviation, a study was conducted to evaluate proposed tenets of HAT. Participants performed a flight-following task at a ground station both with and without HAT features enabled. Overall, participants preferred the ground station with HAT features enabled over the station without the HAT features. Participants reported that the HAT displays and automation were preferred for keeping up with operationally important issues. Additionally, participants reported that the HAT displays and automation provided enough situation awareness to complete the task, reduced the necessary workload and were efficient. Overall, there was general agreement that HAT features supported teaming with the automation. These results will be used to refine and expand our proposed framework for human-autonomy teaming.

Human-Autonomy Teaming (HAT)↗