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NASA NTRS2020

Ellicott City Disasters II: Enhancing a Statistical Flood Risk Model to Continue Improving Early Warning Systems and Public Safety in Ellicott City, Maryland

As flooding events in the United States grow in frequency and intensity, the use of technological advancements and applied science are increasingly necessary for effective flood monitoring and warning systems. The NASA DEVELOP Ellicott City Disasters II project investigated the use of machine learning for applications in flood risk detection to support the improvement of early warning systems. To strengthen the efforts of the Howard County Office of Emergency Management (OEM) in building a more robust flood monitoring system, the project improved the original statistical flood risk model, FLuME (Flood Learning Model Environment), programmed by the first DEVELOP term The enhancements incorporated an additional six years of precipitation and soil moisture data from the North American Land Data Assimilation System (NLDAS), modeled using Aqua Advanced Microwave Scanning Radiometer for EOS and Tropical Rainfall Measuring Mission TRMM Microwave Imager. These Earth observations were supplemented by stream gauge data from the OEM and the US Geological Survey. The resultant flood risk model FLASH (Flood Learning Environment and Severity Assessment Hub) was trained to evaluate input variables and predict stage height in Ellicott City in real time. The addition of an advanced deep learning framework known as long short-term memory improved the model’s ability to capture relationships between variables. To assess the effectiveness of the new model, FLASH produced a model efficiency metric of 0.99, a significant improvement over the 0.85 value produced by the previous model. The project assisted the OEM in pursuing the integration of open data and NASA Earth observations into a threat matrix capable of informing near real-time decision making.

Disasters↗

NASA NTRSDate not supplied

Initial Development of A Digital Twin Model for an Electrified Aircraft Propulsion Emulation Rig

In support of aviation fuel burn and emission reduction goals, NASA is pursing high-payoff research investments that promise to transform aviation. This includes investments in Electrified Aircraft Propulsion (EAP), which relies on the generation, storage, transmission, and use of electrical power for producing thrust and optimizing propulsion system efficiency. Multiple technology challenges must be addressed to unlock the full potential of EAP. This includes advances in propulsion controls, which will be vital for ensuring coordinated efficient operation of the complex integrated subsystems that comprise EAP architectures. To support EAP controls research, the NASA Glenn Research Center has developed the Hybrid Propulsion Emulation Rig (HyPER). The HyPER laboratory hardware includes shaft-mounted electric machines, power converters, power supplies, power distribution cables, and an energy storage device that can be reconfigured to represent a variety of EAP architectures. It also includes an integrated real-time computer system that hosts developed EAP control software and turbomachinery simulations. This enables the electrical system and rotating shafts of EAP designs to be implemented in actual hardware and integrated with turbomachinery simulations and system-level EAP control logic implemented in software. In this form, the HyPER laboratory provides a partially simulated, partially hardware-in-the-loop test environment enabling the initial development and evaluation of EAP control technology. A prerequisite for the development of EAP control designs is the availability of a system model that accurately reflects the operation of the electrical system hardware. To support this need, a digital twin model of the HyPER electrical system hardware is under development. This model is being coded in the MATLAB Simulink environment and uses the NASA-developed Electrical Modeling and Thermal Analysis Toolbox (EMTAT) to construct a digital twin framework. EMTAT contains generic electrical component building blocks that are simulated at turbomachinery timescales. Associated inputs and outputs allow the blocks to be combined to model complete electrical systems. The EMTAT blocks also contain adjustable internal maps and parameters that can be set to reflect the operation of a specific electrical component. For the HyPER digital twin, the settings of these EMTAT block internal maps and parameters is determined through machine learning approaches applied to characterization run data collected from the laboratory. During characterization runs the laboratory electrical system hardware is subjected to a full range of torque, speed, and power settings. Acquired data is then used to estimate EMTAT block parameters using a variety of machine learning techniques. The resulting digital twin model is found to match the operation of actual HyPER hardware with an accuracy suitable for control development purposes. It also holds promise for other applications including modeling the performance of HyPER laboratory reconfigurations and model-based anomaly detection. Planned follow-on work to automate post-processing of acquired laboratory data to update the HyPER digital twin model will also be presented and discussed.

Electrified Aircraft Propulsion↗

NASA NTRS2003

Practical Formal Verification of Diagnosability of Large Models via Symbolic Model Checking

This document reports on the activities carried out during a four-week visit of Roberto Cavada at the NASA Ames Research Center. The main goal was to test the practical applicability of the framework proposed, where a diagnosability problem is reduced to a Symbolic Model Checking problem. Section 2 contains a brief explanation of major techniques currently used in Symbolic Model Checking, and how these techniques can be tuned in order to obtain good performances when using Model Checking tools. Diagnosability is performed on large and structured models of real plants. Section 3 describes how these plants are modeled, and how models can be simplified to improve the performance of Symbolic Model Checkers. Section 4 reports scalability results. Three test cases are briefly presented, and several parameters and techniques have been applied on those test cases in order to produce comparison tables. Furthermore, comparison between several Model Checkers is reported. Section 5 summarizes the application of diagnosability verification to a real application. Several properties have been tested, and results have been highlighted. Finally, section 6 draws some conclusions, and outlines future lines of research.

Cavada, Roberto↗

NASA NTRS1989

Report on the 2-D Model Intercomparison Workshop held January 11-16, 1987 in Fort Myers Beach, Florida, chapter 2

In the attempts to understand the processes which affect ozone in the stratosphere, and to predict future ozone levels, a variety of computer models were developed and employed. To oversimplify somewhat, the models range from 1-D models with intricately detailed photochemistry but only the crudest representation of transport, to 3-D general circulation models with intricately detailed dynamics and no photochemistry at all. Each of these has its appropriate uses. It was felt that there was much to be gained from intermediate models incorporating an extensive treatment of photochemistry with a dynamical framework which at least recognizes that atmospheric motions are advective as well as diffusive, and that both chemistry and dynamics are subject to latitudinal and seasonal variations. Thus the 2-D, zonally averaged models have begun to play a larger role in the last seven years, both in attempting to understand observed distributions of trace species and in attempting to assess the probable effects of antropogenic perturbations. There are many choices to be made in developing the model. It was the purpose of the 2-D model Intercomparison Workshop to permit discussion of the choices made and the behavior of the resulting models. The goal was not to identify a best set of choices, but rather to identify areas in which the models are sensitive to the choices made, and to develop a sense of where these models as a class do well or poorly in simulating the observed atmosphere.

Guthrie, Paul D.↗

NASA NTRSDate not supplied

Wall-Modeled Large-Eddy Simulations of a Swept Wing with Leading-Edge Ice

The aerodynamic performance of a swept wing derived from the Common Research Model wing with high-fidelity and smooth leading-edge ice shapes is evaluated using wall-modeled large-eddy simulations. Two mesh paradigms are considered within the Launch, Ascent, and Vehicle Aerodynamics framework: structured hybrid curvilinear body-fitted/immersed-boundary and unstructured Voronoi. The curvilinear approach models the effect of the ice on the flow through penalty terms and the Voronoi strategy includes the ice representation directly in the mesh.Aerodynamic loads, surface pressure profiles, and skin-friction streamlines are compared against experimental results collected at the Wichita State University wind tunnel at a Mach number of 0.18 and a Reynolds number per mean aerodynamic chord of 1.6 million. For the curvilinear simulations with high-fidelity ice, results are sensitive to spanwise mesh resolution near the leading edge, where insufficient spanwise resolution leads to a spanwise running vortexaft of the high-fidelity ice, which is partially removed with increasing spanwise resolution. For the Voronoi simulations with high-fidelity ice, refining the mesh led to good convergence towards the experimental results with the best case demonstrating a maximum discrepancy of three lift counts relative to the experiment across a range of angles of attack from about 5 to 24 degrees, validating the shrink-wrapping procedure used for the ice. For the smooth ice, the curvilinear approach consistently under-predicted the upper surface suction leading to an under-prediction in lift, but did not demonstrate the pronounced over-prediction in upper surface suction near the leading edge observed in the Voronoi simulations.

AATT↗

NASA NTRS1970

A conceptual framework for transportation research at Mississippi Test Facility of NASA

Constructing models of transportation system in US

Arman, A.↗

NASA NTRS2018

(GO)2-SIM: a GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise.Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidarâ€“radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8% of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40%. So, while hydrometeor-phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

Lamer, K.↗

NASA NTRS2007

A Spectacular Radio Flare from XRF 050416a at 40 Days and Implications for the Nature of X-Ray Flashes

We present detailed optical, near-infrared, and radio observations of the X-ray flash 050416a obtained with Palomar and Siding Springs Observatories as well as HST and the VLA, placing this event among the best-studied X-ray flashes to date. In addition, we present an optical spectrum from Keck LRIS from which we measure the redshift of the burst, Z=0.6528. At this redshift the isotropic-equivalent prompt energy release was about 10(exp 51) erg, and using a standard afterglow synchrotron model we find that the blastwave kinetic energy is a factor of 10 larger, E-K,iso approximately equals 10 (exp 52) erg. The lack of an observed jet break to t - 20 days indicates that the opening angle is larger than 7 deg and the total beaming-corrected relativistic energy is larger than 10 exp (50) erg. We further show that the burst produced a strong radio flare at t is similar to 40 days accompanied by an observed flattening in the X-ray band which we attribute to an abrupt circumburst density jump or an episode of energy injection (either from a refreshed shock or off-axis ejecta). Late-time observations with HST show evidence for an associated supernova with peak optical luminosity roughly comparable to that of SN 1998bw. Next, we show that the host galaxy of XRF 050416a is actively forming stars at a rate of at least 2 M-solar per year with a luminosity of L-B is similar to 0.5L* and metallicity of Z is similar to 0.2-0.8 Z-solar. Finally, we discuss the nature of XRF 050416a in the context of short-hard gamma-ray bursts and under the framework of off-axis and dirty fireball models for X-ray flashes.

Soderberg, A. M.↗

NASA NTRS2007

LMI-Based Generation of Feedback Laws for a Robust Model Predictive Control Algorithm

This technical note provides a mathematical proof of Corollary 1 from the paper 'A Nonlinear Model Predictive Control Algorithm with Proven Robustness and Resolvability' that appeared in the 2006 Proceedings of the American Control Conference. The proof was omitted for brevity in the publication. The paper was based on algorithms developed for the FY2005 R&TD (Research and Technology Development) project for Small-body Guidance, Navigation, and Control [2].The framework established by the Corollary is for a robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems that guarantees the resolvability of the associated nite-horizon optimal control problem in a receding-horizon implementation. Additional details of the framework are available in the publication.

algorithms↗

NASA NTRS2017

Application of Machine Learning to Rotorcraft Health Monitoring

Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.

machine learning↗

NASA NTRS2024

Design, Development, and Use of a Lunar Lander Simulation for NASA's Artemis Program

This paper describes the design, development, and initial use of a generalized and configurable lunar lander simulation to support NASA’s Artemis Program. This simulation is being developed for the Crew Compartment Office (CrewCo) in the Human Landing Systems (HLS) program and is called the HLS CrewCo Lander Simulation (HCLS). The HCLS provides insight into the challenges associated with returning humans to the Moon and the particular difficulties of operating at the Lunar South Pole. A generalized lunar landing spacecraft based on a government reference design has been modeled but the simulation can be modified and adapted to model vendor designs as well. The simulation architecture and toolsets provide a flexible framework that allows for quickly prototyping and evaluating various aspects of a piloted lunar landing system. This includes the modeling of all principal human controlled flight phases: rendezvous and docking with crew transfer systems; docked orbital outpost operations; undocking and lunar transfer; lunar orbital operations; lunar deorbit, descent, and landing (DDL); lunar surface operations; lunar ascent; and return to the orbital outpost. The flexibility of the simulation allows for the integrated evaluation of potential vehicle subsystems, crew displays, guidance and control modes, and trajectory designs. The purpose of the HCLS is not to design the ideal lunar lander, but rather to understand the strengths and weaknesses of vehicle design choices.

Edwin Z Crues↗

NASA NTRS2023

A Performance Analysis on Soil Dielectric Models Over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM < 15%) and organic soils (SOM ≥ 15%) would be the preferred option.

Soil Moisture↗

NASA NTRS2024

Design, Development, and Use of a Lunar Lander Simulation for NASA’s Artemis Program

James Gentile↗

NASA NTRS2020

Validation on the Europa Clipper Mission in the Formulation Phase

The Europa Clipper mission will perform a detailed investigation of Europa’s environment and habitability by placing a spacecraft into a looping orbit around Jupiter and performing repeated close Europa flybys. The Europa Clipper Project Verification and Validation (V&V) team is responsible for verifying that the Project System is compliant with the science and engineering requirements levied on it and validating that it is capable of meeting the mission objectives. Europa Clipper has developed a V&V framework to ensure that mission capabilities are realized at a reasonable confidence level prior to launch. The V&V team will coordinate the validation activities defined by this framework throughout the project lifecycle as the spacecraft evolves from design to implementation and operations. Validation of space missions is typically grouped into three categories: 1) requirements validation, 2) model validation, and 3) system validation. System validation, along with verification activities, are often the primary focus of V&V programs and these efforts peak in the implementation phase. However, the increasing reliance of exploration missions on models and simulations, and the complexity of the Europa Clipper mission, have dictated attention to requirements and model validation in the formulation phase. This paper discusses the development of the Europa Clipper V&V framework with emphasis on the validation of models and simulations, and of requirements. Approaches include the adaptation of institutional guidelines, such as those in the NASA-STD-7009 document, to create a Project Model V&V Plan and the use of tools such as DOORS NG to plan model validation and derive requirement quality metrics.

Srivastava, Priyanka↗

NASA NTRS1991

Baryon isocurvature scenario in inflationary cosmology - A particle physics model and its astrophysical implications

A phenomenological model to produce isocurvature baryon-number fluctuations is proposed in the framework of inflationary cosmology. The resulting spectrum of density fluctuation is very different from the conventional Harrison-Zel'dovich shape. The model, with the parameters satisfying several requirements from particle physics and cosmology, provides an appropriate initial condition for the minimal baryon isocurvature scenario of galaxy formation discussed by Peebles.

Yokoyama, Jun'ichi↗

NASA NTRS1992

Assimilation of a knowledge base and physical models to reduce errors in passive-microwave classifications of sea ice

An expert system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions upon the ice classification, modifies parameters in the ice algorithm, determines a `confidence' measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing non-expert data users with a means of assessing the usefulness of the classification results for their applications.

Maslanik, J. A.↗

NASA NTRS2016

Hemispheric Differences in Tropical Lower Stratospheric Transport and Tracers Annual Cycle

Transport of long-lived tracers (such as O, CO, and N O) in the lower stratosphere largely determines the composition of the entire stratosphere. Stratospheric transport includes the mean residual circulation (with air rising in the tropics and sinking in the polar and middle latitudes), plus two-way isentropic (quasi-horizontal) mixing by eddies. However, the relative importance of two transport components remains uncertain. Previous studies quantified the relative role of these processes based on tropics-wide average characteristics under common assumption of well-mixed tropics. However, multiple instruments provide us with evidence that show significant differences in the seasonal cycle of ozone between the Northern (0-20N) and Southern (0-20S) tropical (NT and ST respectively) lower stratosphere. In this study we investigate these differences in tracer seasonality and quantify transport processes affecting tracers annual cycle amplitude using simulations from Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and Whole Atmosphere Community Climate Model (WACCM) and compare them to observations from the Microwave Limb Sounder (MLS) on the Aura satellite. We detect the observed contrast between the ST and NT in GEOSCCM and WACCM: annual cycle in ozone and other chemical tracers is larger in the NT than in the ST but opposite is true for the annual cycle in vertical advection. Ozone budgets in the models, analyzed based on the Transformed Eulerian Mean (TEM) framework, demonstrate a major role of quasi-horizontal mixing vertical advection in determining the NTST ozone distribution and behavior. Analysis of zonal variations in the NT and ST ozone annual cycles further suggests important role of North American and Asian Summer Monsoons (associated with strong isentropic mixing) on the lower stratospheric ozone in the NT. Furthermore, multi model comparison shows that most CCMs reproduce the observed characteristic of ozone annual cycle quite well. Thus, latitudinal variations within the tropics have to be considered in order to understand the balance between upwelling and quasi- horizontal mixing in the tropical lower stratosphere and the paradigm of well mixed tropics has to be reconsidered.

Chemistry-Climate Models↗

NASA NTRS2020

Towards a Soil Moisture Drought Monitoring System for South Korea

The Korea Land Data Assimilation System (KLDAS) has been established for agricultural drought (i.e. soil moisture deficit) monitoring in South Korea, running the Noah-MP land surface model within the NASA Land Information System (LIS) framework with the added value of local precipitation forcing dataset and soil texture maps. KLDAS soil moisture is benchmarked against three global products: the Global Land Data Assimilation System (GLDAS), the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), and the European Space Agency Climate Change Initiative (ESA CCI) satellite product. The evaluation is performed using in situ measurements for 2013–2015 and one month standardized precipitation index (SPI-1) for 1982–2016, focusing on four major river basins in South Korea. The KLDAS outperforms all benchmark products in capturing soil moisture states and variability at a basin scale. Compared to GLDAS and FLDAS products, the EAS CCI product is not feasible for long term agricultural monitoring due to lower data quality for early periods (1979–1991) of soil moisture estimates. KLDAS shows that the most recent 2015 drought event leads to highest drought areas in the Han and Geum River basins in the past 35 years. This work supports KLDAS as an effective agricultural drought monitoring system to provide continuous regional high-resolution soil moisture estimates in South Korea.

Agricultural drought↗