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NASA NTRSDate not supplied

Aviary: An Open-Source Multidisciplinary Design, Analysis, and Optimization Tool for Modeling Aircraft with Analytic Gradients

Aviary is an open-source aircraft design, analysis, and optimization tool built on OpenMDAO and leveraging the legacy tools of FLOPS and GASP. This presentation will introduce the basics of Aviary, more details for which can be found in the paper. Specifically, this presentation will discuss an overview of why new aircraft design tools are necessary and how Aviary meets those needs, the structure and subsystems of Aviary, and the validation and interface that Aviary provides.

optimization↗

NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗

NASA NTRS2022

A Dynamic Landslide Hazard Monitoring Framework for the Lower Mekong Region

The Lower Mekong region is one of the most landslide-prone areas of the world. Despite the need for dynamic characterization of landslide hazard zones within the region, it is largely understudied for several reasons. Dynamic and integrated understanding of landslide processes requires landslide inventories across the region, which have not been available previously. Computational limitations also hamper regional landslide hazard assessment, including accessing and processing remotely sensed information. Finally, open-source software and modelling packages are required to address regional landslide hazard analysis. Leveraging an open-source data-driven global Landslide Hazard Assessment for Situational Awareness model framework, this study develops a region-specific dynamic landslide hazard system leveraging satellite-based Earth observation data to assess landslide hazards across the lower Mekong region. A set of landslide inventories were prepared from high-resolution optical imagery using advanced image-processing techniques. Several static and dynamic explanatory variables (i.e., rainfall, soil moisture, slope, relief, distance to roads, distance to faults, distance to rivers) were considered during the model development phase. An extreme gradient boosting decision tree model was trained for the monsoon period of 2015–2019 and the model was evaluated with independent inventory information for the 2020 monsoon period. The model performance demonstrated considerable skill using receiver operating characteristic curve statistics, with Area Under the Curve values exceeding 0.95. The model architecture was designed to use near-real-time data, and it can be implemented in a cloud computing environment (i.e., Google Cloud Platform) for the routine assessment of landslide hazards in the Lower Mekong region. This work was developed in collaboration with scientists at the Asian Disaster Preparedness Center as part of the NASA SERVIR Program’s Mekong hub. The goal of this work is to develop a suite of tools and services on accessible open-source platforms that support and enable stakeholder communities to better assess landslide hazard and exposure at local to regional scales for decision making and planning.

Nishan Kumar Biswas↗

NASA NTRS2018

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currentlyconfigured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean throughthe accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two(vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection_divergence, blowingsnow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim,and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus "median" daily snowfall dataset from these reanalyses.The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate_strong correlations and root mean squared errors of 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Petty, Alek A.↗

NASA NTRS2021

A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy

We report on Bayesian estimation of the radius, mass, and hot surface regions of the massive millisecond pulsar PSR J0740+6620, conditional on pulse-profile modeling of Neutron Star Interior Composition Explorer X-ray Timing Instrument event data. We condition on informative pulsar mass, distance, and orbital inclination priors derived from the joint North American Nanohertz Observatory for Gravitational Waves and Canadian Hydrogen Intensity Mapping Experiment/Pulsar wideband radio timing measurements of Fonseca et al. We use XMM-Newton European Photon Imaging Camera spectroscopic event data to inform our X-ray likelihood function. The prior support of the pulsar radius is truncated at 16 km to ensure coverage of current dense matter models. We assume conservative priors on instrument calibration uncertainty. We constrain the equatorial radius and mass of PSR J0740+6620 to be-+12.390.981.30km and-+2.0720.0660.067Me respectively, each reported as the posterior credible interval bounded by the 16% and 84% quantiles, conditional on surface hot regions that are non-overlapping spherical caps of fully ionized hydrogen atmosphere with uniform effective temperature; a posteriori, the temperature is=-+TlogK5.99100.060.05([])for each hot region. All software for the X-ray modeling framework is open-source and all data, model, and sample information is publicly available, including analysis notebooks and model modules in the Python language. Our marginal likelihood function of mass and equatorial radius is proportional to the marginal joint posterior density of those parameters(within the prior support)and can thus be computed from the posterior samples.

Millisecond pulsars↗

NASA NTRS2024

Adiabatic Gravitational Waveform Model for Compact Objects Undergoing Quasi-Circular Inspirals Into Rotating Massive Black Holes

We present bhpwave: a new python-based, open-source tool for generating the gravitational waveforms of stellar-mass compact objects undergoing quasicircular inspirals into rotating massive black holes. These binaries, known as extreme-mass-ratio inspirals (EMRIs), are exciting mHz gravitational wave sources for future space-based detectors such as the Laser Interferometer Space Antenna (LISA). Relativistic models of EMRI gravitational wave signals are necessary to unlock the full scientific potential of mHz detectors, yet few open-source EMRI waveform models exist. Thus we built bhpwave, which uses the adiabatic approximation from black hole perturbation theory to rapidly construct gravitational waveforms based on the leading-order inspiral dynamics of the binary. In this work, we present the theoretical and numerical foundations underpinning bhpwave. We also demonstrate how bhpwave can be used to assess the impact of EMRI modeling errors on LISA gravitational wave data analysis. In particular, we find that for retrograde orbits and slowly spinning black holes we can mismodel the gravitational wave phasing by as much as ∼10 radians without significantly biasing EMRI parameter estimation.

Zachary Nasipak↗

NASA NTRS2024

Hector V3.2.0: Functionality and Performance of a Reduced-Complexity Climate Model

Hector is an open-source reduced complexity climate-carbon cycle model that models critical Earth system processes on a global and annual basis. Here we present an updated version of the model, Hector V3.2.0 (hereafter Hector V3) and document its new features, implementation of new science, and performance. Significant new features include permafrost thaw, a reworked energy balance submodel, and updated parameterizations throughout. Hector V3 results are in good general agreement with historical observations of atmospheric CO2 concentrations and global mean surface temperature, and its future temperature projections are consistent with more complex Earth System Model output data from the Sixth Coupled Model Intercomparison Project. We show that Hector V3 is a fully open source, flexible, performant, and robust simulator of global climate changes, note its limitations, and discuss future areas of improvement and research with respect to the model’s scientific, stakeholder, and educational priorities.

Kayln Dorheim↗

NASA NTRS2016

Biomechanical Modeling of Split-leg Squat and Heel Raise on the Hybrid Ultimate Lifting Kit (HULK)

Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize the loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and musculoskeletal forces that occur during exercises performed on the prototype devices. Computational models currently use OpenSim software, an open source code for musculoskeletal modeling, with biomechanical input data from subjects for estimation of muscle and joint loads. Subjects are instrumented with reflective markers for motion capture data collection while exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device. Ground reaction force data is collected with force plates under the feet and device loading is recorded through load cells internal to the HULK. This data is input into the OpenSim biomechanical model, which has been scaled to match the anthropometrics of the test subject, to calculate the loads on the body. Multiple exercises are performed and evaluated during a test session such as a full squat, single leg squat, heel raise and dead lift. Variables for these exercises include applied device load, narrow or wide foot stance, slow or fast cadence and the harness or long bar interface between the test subject and the device. Data from free weights are compared to the resistively loaded exercise device. The focus of this presentation is to summarize the results from the single-leg squat and heel raise exercises performed during three sessions occurring in 2015. Differences in loading configuration, cadence and stance produce differences in kinematics, joint toques and force and muscle forces.

computational modeling↗

NASA NTRS1997

Design of An Improved Miniature Ion Neutral Mass Spectrometer for NASA Applications

The ion optics of NASA's Ion Neutral Mass Spectrometer (INMS) sensor was simulated with three dimensional models of the open source, the quadrupole deflector, the exit lens system and the quadrupole mass analyzer to design more compact models with lower weight. Comparison of calculated transmission with experimental results shows good agreement. Transmission analyses with varying geometrical parameters and voltages throw light on possible ways of reducing the size of the sensor. Trajectories of ions of mass 1-99 amu were simulated to analyze and optimize transmission. Analysis of open source transmission with varying angle of attack shows that the angular acceptance can be considerably increased by programming the voltages on the ion trap/ collimator. Analysis of transmission sensitivity to voltages and misalignments of the quadrupole deflector rods indicate that increased transmission is possible with a geometrically asymmetrical deflector and a deflector can be designed with much lower sensitivities of transmission. Bringing the disks closer together can decrease the size of the quadrupole deflector and also increase transmission. The exit lens system can be redesigned to be smaller by eliminating at least one electrode entirely without loss of transmission. Ceramic materials were investigated to find suitable candidates for use in the construction of lighter weight mass spectrometer. A high-sensitivity, high-resolution portable gas chromatograph mass spectrometer with a mass range of 2-700 amu has been built and will be commercialized in Phase 3.

Swaminathan, Viji K.↗

NASA NTRSDate not supplied

Dissemination of Global Flood Severity and Surface Water Mapping using Remote Sensing Data to Global Stakeholders

Flooding is a natural event that occurs frequently with high severity worldwide, responsible for significant societal and economic impacts. Disaster managers face significant challenges managing essential information for preparedness, response, and recovery efforts. The development of an open access, global flood alerting system for effective identification of flood impacted areas, classification of potential impacts, and the formulation of effective emergency response measures requires the incorporation of a wide variety of flood models and remote sensing data sources from multiple platforms. NASA is currently funding projects focused on flood forecasting, post-event flood mapping, flood depth estimation and pre-event flood severity estimation using Earth observation (EO) datasets and derived flood products. A new initiative in the Disasters Program is underway to disseminate flood products from different hydrologic models and sensors to global stakeholders via Pacific Disaster Center’s DisasterAWARE®, NASA’s Disasters Mapping Portal and potentially other mechanisms. This initiative focuses on improving response capacity and use of EO products in near real-time by a broader community for resource planning in case of extreme events. As part of this initiative, we have deployed Model of Models (MoM) – an open-source ensemble approach, that integrates outputs from hydrologic models and EO data from optical imagery to assess flood severity daily at sub-watershed level globally. The MoM output is integrated with the incident event system of DisasterAWARE to generate flood severity risk and flood impact boundaries, which are disseminated via the DisasterAWARE platform to different stakeholders globally for decision-making and response efforts. The next step will focus on using MoM outputs to estimate flood depth and extent mapping using high-resolution Synthetic Aperture Radar imagery, impact assessment using optical imagery and population datasets, and damage estimation using critical infrastructure datasets, which would be disseminated via DisasterAWARE to decision-makers, emergency managers and first responders around the world.

flood↗

NASA NTRSDate not supplied

2022 Spring Internship Exit Presentation

As efforts of the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) continue to digitize the air traffic management (ATM) domain, there is countless times of need for downstream natural language processing (NLP) tasks such as named entity recognition, text summarization, classification, and more. Although there are a plethora of open-sourced pre-trained transformer models in the NLP field such as BERT, RoBERTa, XLNet, and GPT-3, these models are trained on general corpora and perform poorly on domain-specific terminology and phraseology seen in ATM documents such as Notice to Airmen (NOTAMs) and Letters of Agreement (LoA). Our proposed research objective will be to first gather a large corpus of air traffic management related documents, orders, notices, books, technical papers, conference papers, articles, and other miscellaneous sources of text data from the FAA, NASA, and accredited conference and publication societies. After gathering this data, many steps will have to be taken to collate and preprocess the data into a format understandable by our test transformer models. Thirdly, we will set up training pipelines to train the RoBERTa model on its unsupervised training task masked language modelling (MLM) using resources provided by the NASA Advanced Supercomputing (NAS) facilities. Finally, these fine-tuned transformer models will be evaluated on their performance on down-stream NLP tasks as mentioned above, to show whether they will be effective when working with ATM related data or not. Once complete, this model could be made open-sourced on the HuggingFace website, where the rest of the ATM community can access and utilize this tool.

NLP↗

NASA NTRS2016

The Role of Flow Diagnostic Techniques in Fan and Open Rotor Noise Modeling

A principal source of turbomachinery noise is the interaction of the rotating and stationary blade rows with the perturbations in the airstream through the engine. As such, a lot of research has been devoted to the study of the turbomachinery noise generation mechanisms. This is particularly true of fan and open rotors, both of which are the major contributors to the overall noise output of modern aircraft engines. Much of the research in fan and open rotor noise has been focused on developing theoretical models for predicting their noise characteristics. These models, which run the gamut from the semi-empirical to fully computational ones, are, in one form or another, informed by the description of the unsteady flow-field in which the propulsors (i.e., the fan and open rotors) operate. Not surprisingly, the fidelity of the theoretical models is dependent, to a large extent, on capturing the nuances of the unsteady flowfield that have a direct role in the noise generation process. As such, flow diagnostic techniques have proven to be indispensible in identifying the shortcoming of theoretical models and in helping to improve them. This presentation will provide a few examples of the role of flow diagnostic techniques in assessing the fidelity and robustness of the fan and open rotor noise prediction models.

Noise Prediction↗

NASA NTRS2016

Squat Biomechanical Modeling Results from Exercising on the Hybrid Ultimate Lifting Kit

Long duration space travel will expose astronauts to extended periods of reduced gravity. Since gravity is not present to aid loading, astronauts will use resistive and aerobic exercise regimes for the duration of the space flight to minimize loss of bone density, muscle mass and aerobic capacity that occurs during exposure to a reduced gravity environment. Unlike the International Space Station (ISS), the area available for an exercise device in the next generation of spacecraft is limited and therefore compact resistance exercise device prototypes are being developed. The Advanced Resistive Exercise Device (ARED) currently on the ISS is being used as a benchmark for the functional performance of these new devices. Biomechanical data collection and computational modeling aid the device design process by quantifying the joint torques and the musculoskeletal forces that occur during exercises performed on the prototype devices. The computational models currently under development utilize the OpenSim software, an open source code for musculoskeletal modeling, with biomechanical input data from test subjects for estimation of muscle and joint loads. The subjects are instrumented with reflective markers for motion capture data collection while exercising on the Hybrid Ultimate Lifting Kit (HULK) prototype device. Ground reaction force data is collected with force plates under the feet and device loading is recorded through load cells internal to the HULK. Test variables include applied device load, narrow or wide foot stance, slow or fast cadence and the harness or long bar interface between the test subject and the device. Data is also obtained using free weights for a comparison to the resistively loaded exercise device. This data is input into the OpenSim biomechanical model, which has been scaled to match the anthropometrics of the test subject, to calculate the body loads. The focus of this presentation is to summarize the results from the full squat exercises across the different test variables.

Bioengineering↗

NASA NTRS2010

DigitalHuman (DH): An Integrative Mathematical Model ofHuman Physiology

Mathematical models and simulation are important tools in discovering the key causal relationships governing physiological processes and improving medical intervention when physiological complexity is a central issue. We have developed a model of integrative human physiology called DigitalHuman (DH) consisting of -5000 variables modeling human physiology describing cardiovascular, renal, respiratory, endocrine, neural and metabolic physiology. Users can view time-dependent solutions and interactively introduce perturbations by altering numerical parameters to investigate new hypotheses. The variables, parameters and quantitative relationships as well as all other model details are described in XML text files. All aspects of the model, including the mathematical equations describing the physiological processes are written in XML open source, text-readable files. Model structure is based upon empirical data of physiological responses documented within the peer-reviewed literature. The model can be used to understand proposed physiological mechanisms and physiological interactions that may not be otherwise intUitively evident. Some of the current uses of this model include the analyses of renal control of blood pressure, the central role of the liver in creating and maintaining insulin resistance, and the mechanisms causing orthostatic hypotension in astronauts. Additionally the open source aspect of the modeling environment allows any investigator to add detailed descriptions of human physiology to test new concepts. The model accurately predicts both qualitative and more importantly quantitative changes in clinically and experimentally observed responses. DigitalHuman provides scientists a modeling environment to understand the complex interactions of integrative physiology. This research was supported by.NIH HL 51971, NSF EPSCoR, and NASA

Hester, Robert L.↗

NASA NTRS2012

Crossing the Virtual World Barrier with OpenAvatar

There are multiple standards and formats for 3D models in virtual environments. The problem is that there is no open source platform for generating models out of discrete parts; this results in the process of having to "reinvent the wheel" when new games, virtual worlds and simulations want to enable their users to create their own avatars or easily customize in-world objects. OpenAvatar is designed to provide a framework to allow artists and programmers to create reusable assets which can be used by end users to generate vast numbers of complete models that are unique and functional. OpenAvatar serves as a framework which facilitates the modularization of 3D models allowing parts to be interchanged within a set of logical constraints.

Joy, Bruce↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗

NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Data management↗