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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
NASA NTRS2002

Transition from Gaseous Compounds to Aerosols in Titan's Atmosphere

We investigate the chemical transition of simple molecules like C2H2 and HCN into aerosol particles in the context of Titan's atmosphere. Experiments that synthesize analogs (tholins) for these aerosols can help understand and constrain these polymerization mechanisms. Using information available from these experiments, we suggest chemical pathways that can link simple molecules to macromolecules, that will be the precursors to aerosol particles: polymers of acetylene and cyanoacetylene, polycyclic aromatics (PAHs), polymers of HCN and other nitriles, and polynes. Although our goal here is not to build a detailed kinetic model for this transition, we propose parameterizations to estimate the production rates of these macromolecules, their C/N and C/H ratios, and the loss of parent molecules (C2H2, HCN, HC3N and other nitriles, C6H6) from the gas phase to the haze. We use a 1-dimensional photochemical model of Titan's atmosphere to estimate the formation rate of precursors macromolecules. We find a production zone slightly lower than 200 km altitude with a total production rate of 4 x 10(exp -14) g/ sq cm s and a C/N approx. = 4. These results are compared with experimental data, and to microphysical models requirements. The Cassini/Huygens mission will bring a detailed picture of the haze distribution and properties, that will be a great challenge for our understanding of those chemical processes.

Lebonnois, Sebastien↗
NASA NTRS2009

Formal Methods for Automated Diagnosis of Autosub 6000

This is a progress report on applying formal methods in the context of building an automated diagnosis and recovery system for Autosub 6000, an Autonomous Underwater Vehicle (AUV). The diagnosis task involves building abstract models of the control system of the AUV. The diagnosis engine is based on Livingstone 2, a model-based diagnoser originally built for aerospace applications. Large parts of the diagnosis model can be built without concrete knowledge about each mission, but actual mission scripts and configuration parameters that carry important information for diagnosis are changed for every mission. Thus we use formal methods for generating the mission control part of the diagnosis model automatically from the mission script and perform a number of invariant checks to validate the configuration. After the diagnosis model is augmented with the generated mission control component model, it needs to be validated using verification techniques.

Ernits, Juhan↗
NASA NTRS2020

Space-Based Observations for Understanding Changes in the Arctic-Boreal Zone

Observations taken over the last few decades indicate that dramatic changes are occurring in the ArcticBoreal Zone (ABZ), which are having significant impacts on ABZ inhabitants, infrastructure, flora and fauna, and economies. While suitable for detecting overall change, the current capability is inadequate for systematic monitoring and for improving processbased and largescale understanding of the integrated components of the ABZ, which includes the cryosphere, biosphere, hydrosphere, and atmosphere. Such knowledge will lead to improvements in Earth system models, enabling more accurate prediction of future changes and development of informed adaptation and mitigation strategies. In Duncan et al. (2020), we review the strengths and limitations of current spacebased observational capabilities for several important ABZ components and make recommendations for improving upon these current capabilities. We recommend an interdisciplinary and stepwise approach to develop a comprehensive ABZ Observing Network (ABZON), beginning with an initial focus on observing networks designed to gain processbased understanding for individual ABZ components and systems that can then serve as the building blocks for a comprehensive ABZON.

Artic-Boreal↗
NASA NTRS2003

Applying a Crew Accommodations Resource Model to Future Space Vehicle Research

The success of research and development for human space flight depends heavily on modeling. In addition, the use of such models is especially critical at the earliest phase of research and development of any manned vehicle or habitat. NASA is currently studying various innovative and futuristic propulsion technologies to enable further exploration of space by untended as well as tended vehicles. Details such as vehicle mass, volume, shape and configuration are required variables to evaluate the success of the propulsion concepts. For tended vehicles, the impact of the crew's requirements on those parameters must be included. This is especially important on long duration missions where the crew requirements become more complex. To address these issues, a crew accommodations resource model, developed as a mission planning tool for human spaceflight (Stillwell, Boutros, & Connolly), was applied to a reference mission in order to estimate the volume and mass required to sustain a crew for a variety of long duration missions. The model, which compiled information from numerous different sources and contains various attributes which can be modified to enable comparisons across different dimensions, was instrumental in deriving volume and mass required for a tended long duration space flight. With the inclusion of some additional variables, a set of volume and mass requirements were provided to the project. If due consideration to crew requirements for volume and mass had not been entertained, the assumptions behind validation of the propulsion technology could have been found to be incorrect, possibly far into development of the technology or even into the design and build of test vehicles. The availability and use of such a model contributes significantly by increasing the accuracy of human space flight research and development activities and acts as a cost saving measure by preventing inaccurate assumptions from driving design decisions.

Blume, Jennifer Linda↗
NASA NTRS2008

The Integrated Medical Model: Statistical Forecasting of Risks to Crew Health and Mission Success

The Integrated Medical Model (IMM) helps capture and use organizational knowledge across the space medicine, training, operations, engineering, and research domains. The IMM uses this domain knowledge in the context of a mission and crew profile to forecast crew health and mission success risks. The IMM is most helpful in comparing the risk of two or more mission profiles, not as a tool for predicting absolute risk. The process of building the IMM adheres to Probability Risk Assessment (PRA) techniques described in NASA Procedural Requirement (NPR) 8705.5, and uses current evidence-based information to establish a defensible position for making decisions that help ensure crew health and mission success. The IMM quantitatively describes the following input parameters: 1) medical conditions and likelihood, 2) mission duration, 3) vehicle environment, 4) crew attributes (e.g. age, sex), 5) crew activities (e.g. EVA's, Lunar excursions), 6) diagnosis and treatment protocols (e.g. medical equipment, consumables pharmaceuticals), and 7) Crew Medical Officer (CMO) training effectiveness. It is worth reiterating that the IMM uses the data sets above as inputs. Many other risk management efforts stop at determining only likelihood. The IMM is unique in that it models not only likelihood, but risk mitigations, as well as subsequent clinical outcomes based on those mitigations. Once the mathematical relationships among the above parameters are established, the IMM uses a Monte Carlo simulation technique (a random sampling of the inputs as described by their statistical distribution) to determine the probable outcomes. Because the IMM is a stochastic model (i.e. the input parameters are represented by various statistical distributions depending on the data type), when the mission is simulated 10-50,000 times with a given set of medical capabilities (risk mitigations), a prediction of the most probable outcomes can be generated. For each mission, the IMM tracks which conditions occurred and decrements the pharmaceuticals and supplies required to diagnose and treat these medical conditions. If supplies are depleted, then the medical condition goes untreated, and crew and mission risk increase. The IMM currently models approximately 30 medical conditions. By the end of FY2008, the IMM will be modeling over 100 medical conditions, approximately 60 of which have been recorded to have occurred during short and long space missions.

Fitts, M. A.↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2019

Myco-Architecture off Planet: Growing Surface Structures at Destination

Our work focused on filling major select key technical knowledge gaps, and technical aspects to be addressed in a Phase II proposal were identified such as the secretion of glues or plastics by the fungi or bacteria to form biocomposites. Specifically, we raised the technical TRL by assessing the growth of in-house mycelial-producing strains on potential food substrates and analyzed the advantages and disadvantages to their use off planet. Growth of the mycelia on sawdust and powdered nutrients including dried, powdered algae, was used as a baseline. We measured growth as a function of temperature, tested the relevant material properties of the mycelia products produced, and measured the mass of input and output volume for production per volume of material. Mycotecture was produced in a bag simulating the proposed mission implementation. Post-production such as heat treatment (as is done with terrestrial mycotecture)was assessed.The material properties of the dried, frozen and baked mycelial outputs included density, strength,thermal insulation and flame retardation. Tensile/compression testing on biocomposite material specimens was performed to obtain three-dimensional elastic constants, as well as to examine deformation and fracture behavior. These mechanical tests were performed on both dormant and activated samples to understand the evolution of the structural material. In addition, experimental data obtained from the mechanical testing was used to build a failure prediction model that accounts for material anisotropy. This aided in future structural design through a quantitative understanding of the mechanical limits of the material. To examine the texture, defects, fracture surfaces of the material, high-resolution microstructural imaging was utilized before and after the mechanical tests. This microstructural analysis informed us about the macro-structural behavior and influenced structural design. Thermal decomposition analysis was also performed tounderstand the thermal limits.

Biocomposites↗
NASA NTRS2020

Toward Transition Modeling in a Hypersonic Boundary Layer at Flight Conditions

An accurate physics-based transition prediction method integrated with computational fluid dynamics (CFD) solvers is pursued for hypersonic boundary layer flows over slender hypersonic vehicles at flight conditions. The geometry and flow conditions are selected to match relevant trajectory locations from the ascent phase of the HIFiRE-1 flight experiment, namely, a 7-degree half-angle cone with 2.5 mm nose radius, freestream Mach numbers in the range of 3.8 – 5.5 and freestream unit Reynolds numbers in the range of 3.3 × 10(exp 6) – 21.4 × 10(exp 6) m(exp -1). Earlier research had shown that the onset of transition during the HIFiRE-1 flight experiment correlated with an amplification factor of N ≈ 13.5 for the planar Mack modes. However, to incorporate the N-factor correlations into a CFD code, we investigate surrogate models for disturbance amplification that avoid the direct computation of stability characteristics. A commonly used approach for low-speed flows is based on an a priori database of stability characteristics for locally similar profiles. However, the results presented in this paper demonstrate that the application of this approach to hypersonic boundary layers over blunt spherical nose-tip cones leads to large, unacceptable errors in the predictions of amplification factors, mainly due to its failure in accounting for the effects of the entropy layer on the boundary-layer profiles along the length of the model. We propose and demonstrate an alternate approach that employs the stability computations for a canonical set of blunt cone configurations to train a physics-informed convolutional neural network model that is shown to provide substantially improved transition predictions for hypersonic flow configurations with entropy-layer effects. Furthermore, the excellent performance of the neural network model is also confirmed for cone configurations with nose radius and half-angle values that do not correspond to those used to build the database. Finally, the convolutional neural network model is shown to outperform the linear stability calculations for underresolved basic states.

Pedro Paredes↗
NASA NTRS2000

Computer Modeling of Real-Time Dynamic Lighting

Space Station tasks involve procedures that are very complex and highly dependent on the availability of visual information. In many situations, cameras are used as tools to help overcome the visual and physical restrictions associated with space flight. However, these cameras are effected by the dynamic lighting conditions of space. Training for these is conditions is necessary. The current project builds on the findings of an earlier NRA funded project, which revealed improved performance by humans when trained with computer graphics and lighting effects such as shadows and glare.

Maida, James C.↗
NASA NTRS1995

Baseline experimental investigation of an electrohydrodynamically assisted heat pipe

The increases in power demand and associated thermal management requirements of future space programs such as potential Lunar/Mars missions will require enhancing the operating efficiencies of thermal management devices. Currently, the use of electrohydrodynamically (EHD) assisted thermal control devices is under consideration as a potential method of increasing thermal management system capacity. The objectives of the currently described investigation included completing build-up of the EHD-Assisted Heat Pipe Test bed, developing test procedures for an experimental evaluation of the unassisted heat pipe, developing an analytical model capable of predicting the performance limits of the unassisted heat pipe, and obtaining experimental data which would define the performance characteristics of the unassisted heat pipe. The information obtained in the currently proposed study will be used in order to provide extensive comparisons with the EHD-assisted performance observations to be obtained during the continuing investigation of EHD-Assisted heat transfer devices. Through comparisons of the baseline test bed data and the EHD assisted test bed data, accurate insight into the performance enhancing characteristics of EHD augmentation may be obtained. This may lead to optimization, development, and implementation of EHD technology for future space programs.

Duncan, A. B.↗
NASA NTRS2012

Linked Open Data in the Global Change Information System (GCIS)

The U.S. Global Change Research Program (http://globalchange.gov) coordinates and integrates federal research on changes in the global environment and their implications for society. The USGCRP is developing a Global Change Information System (GCIS) that will centralize access to data and information related to global change across the U.S. federal government. The first implementation will focus on the 2013 National Climate Assessment (NCA) . (http://assessment.globalchange.gov) The NCA integrates, evaluates, and interprets the findings of the USGCRP; analyzes the effects of global change on the natural environment, agriculture, energy production and use, land and water resources, transportation, human health and welfare, human social systems, and biological diversity; and analyzes current trends in global change, both human-induced and natural, and projects major trends for the subsequent 25 to 100 years. The NCA has received over 500 distinct technical inputs to the process, many of which are reports distilling and synthesizing even more information, coming from thousands of individuals around the federal, state and local governments, academic institutions and non-governmental organizations. The GCIS will present a web-based version of the NCA including annotations linking the findings and content of the NCA with the scientific research, datasets, models, observations, etc. that led to its conclusions. It will use semantic tagging and a linked data approach, assigning globally unique, persistent, resolvable identifiers to all of the related entities and capturing and presenting the relationships between them, both internally and referencing out to other linked data sources and back to agency data centers. The developing W3C PROV Data Model and ontology will be used to capture the provenance trail and present it in both human readable web pages and machine readable formats such as RDF and SPARQL. This will improve visibility into the assessment process, increase understanding and reproducibility, and ultimately increase credibility and trust of the resulting report. Building on the foundation of the NCA, longer term plans for the GCIS include extending these capabilities throughout the U.S. Global Change Research Program, centralizing access to global change data and information across the thirteen agencies that comprise the program.

Tilmes, Curt A.↗
NASA NTRS2002

Supporting the Future Air Traffic Control Projection Process

In air traffic control, projecting what the air traffic situation will be over the next 30 seconds to 30 minutes is a key process in identifying conflicts that may arise so that evasive action can be taken upon discovery of these conflicts. A series of field visits in the Boston and New York terminal radar approach control (TRACON) facilities and in the oceanic air traffic control facilities in New York and Reykjavik, Iceland were conducted to investigate the projection process in two different ATC domains. The results from the site visits suggest that two types of projection are currently used in ATC tasks, depending on the type of separation minima and/or traffic restriction and information display used by the controller. As technologies improve and procedures change, care should be taken by designers to support projection through displays, automation, and procedures. It is critical to prevent time/space mismatches between interfaces and restrictions. Existing structure in traffic dynamics could be utilized to provide controllers with useful behavioral models on which to build projections. Subtle structure that the controllers are unable to internalize could be incorporated into an ATC projection aid.

Davison, Hayley J.↗
NASA NTRS1992

Cost - The challenge for advanced materials and structures

Information is presented on the cost of various aircraft structures, together with methods for predicting and reducing cost. The need for the development of cost models, and of a comparative cost algorithm which could function as an engineering design tool to evaluate different design concepts, is emphasized. Efforts are underway to develop cost models that establish building-block unit cell elements that represent different material forms, geometric shapes, fabrication processes, and methods of assembly, with the purpose of expressing cost per pound or labor per pound data, with physical design and manufacture variables that a designer can visualize.

Davis, John G., Jr.↗
NASA NTRS2017

Review of a Draft - Avoiding the Impossible: Re-focusing a Non-Feasible Mission 2-Hrs into a 3-Day Engineering Session

Concurrent engineering offers a great many benefits to engineers and mission designers throughout the world of aerospace. The only downside of concurrent engineering, and this is somewhat unavoidable, is that you don’t know the results of a design session until the end when it is completed. Usually, this is not a problem – you wouldn’t start building a spacecraft before the design is finished. However, within mass and cost constrained systems, you may end up with a final design that although technically sound – is not feasible due to mass or cost limits. Employing in-session mass and cost models with flexible inputs that refine their estimates and variance as more detailed information comes in throughout a design session allows major design changes to be made when the probability of breaching a mass or cost cap exceeds a threshold level. This enables mission designers to re-focus the study, and avoid spending 3-days with 15 engineers designing a non-feasible mission. By understanding key correlations and nested relationships within mass or cost, and specifically mass or cost allocations per mission element by mission type, it’s possible to get flexible-input, statistically based mass and cost estimates very early in the design process. Baseline models are seeded using mission characteristics and general parameters (outer planetary orbiter-probe mission, $500M cost cap for example) to provide a rough estimate of the expected mass or cost. As information gets solidified during the session, it gets added to the model and the estimates are updated. Continuing the orbiter-probe mission example, modeling probe heat shield cost as a percent of total probe cost, and probe cost as a percent of total flight system cost, and total flight system cost as a percent of total mission cost allows a design team to roll-up solidified information to estimate the probability of fitting within a mass or cost constraint early in a concurrent design session. When only the heat shield cost is known, the variance of the final estimate is higher, whereas when the full probe gets defined, naturally, the variance of the estimate decreases. A methodology, model, verification and demo implementation for cost limit breach are presented.

Hihn, Jairus M.↗
NASA NTRS1996

A study of facilities and fixtures for testing of a high speed civil transport wing component

A study was performed to determine the feasibility of testing a large-scale High Speed Civil Transport wing component in the Structures and Materials Testing Laboratory in Building 1148 at NASA Langley Research Center. The report includes a survey of the electrical and hydraulic resources and identifies the backing structure and floor hard points which would be available for reacting the test loads. The backing structure analysis uses a new finite element model of the floor and backstop support system in the Structures Laboratory. Information on the data acquisition system and the thermal power requirements is also presented. The study identified the hardware that would be required to test a typical component, including the number and arrangement of hydraulic actuators required to simulate expected flight loads. Load introduction and reaction structure concepts were analyzed to investigate the effects of experimentally induced boundary conditions.

Cerro, J. A.↗
NASA NTRS1999

Some Continuum Aspects of Data Assimilation

A long-sought goal in data assimilation is to build more fully the time dimension into the data assimilation process. Truly four-dimensional data assimilation requires evolving second-moment information, namely the estimation error covariance, along with the state estimate itself. The continuum evolution equations for the estimation error covariance constitute a system of partial differential equations in six space dimensions, forced by a model error covariance and by the observations themselves. The high dimensionality of this system poses a difficult computational problem. Numerous methods have been proposed for approximate, discrete solution of this system of equations. Four-dimensional variational schemes solve these equations indirectly, while schemes based on Kalman filtering solve the equations more directly, usually on a small subspace of the full six-dimensional space. In both cases, most proposed solution methods are not derived from the continuum covariance evolution equations themselves. Instead, most methods simply inherit the discretization used for evolving the state estimate, either in the form of a discrete tangent linear model or, in the case of ensemble schemes, in the form of a fully nonlinear discrete model. In this lecture we show that solutions of the continuum covariance evolution equations possess simple properties that are not readily satisfied upon discretization of the equations. we give numerical examples illustrating that "inherited" discretizations sometimes fail to produce meaningfully accurate solutions. Finally, we suggest alternative discretization methods that may yield more faithful approximate solutions of the continuum problem.

Cohn, Stephen E.↗
NASA NTRS2025

Assessment of Model Outcomes Between the Integrated Medical Model (IMM) and the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT)

The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is a computational model that provides human health and medical risk predictions for crewed spaceflight missions. MEDPRAT utilizes discrete event modeling and dynamic probabilistic simulation to predict critical mission outcomes (total medical events, crew health index, quality time lost, loss of crew life, removal to definitive care), condition occurrences, and resource consumption. Input parameters for MEDPRAT include crew attributes (e.g., sex), types of mission activities (e.g., whether and where crew members perform an extravehicular activity (EVA)), available resources, treatment information, and probability distributions for medical conditions. As an evolution of the Integrated Medical Model (IMM), MEDPRAT provides enhanced capabilities and higher fidelity, and incorporates more appropriate assumptions for long-duration spaceflight. IMM is the currently accepted standard for quantifying spaceflight mission medical risk in NASA operations that uses a probabilistic risk assessment (PRA) approach. MEDPRAT builds on the same logical foundation as IMM but implements the model architecture through highly optimized Monte Carlo sampling methods. An analysis is performed comparing the outputs from IMM with those from MEDPRAT V1.0 and V2.0 for the same reference missions in order to quantify similarities and differences in the model outcomes. The juxtaposition between IMM and MEDPRAT V1.0 and 2.0 shown in this report demonstrates that these two models generate very similar results; where differences in outcomes are shown, these are in accordance with the underlying assumptions and differences in the model architectures. This validation effort further establishes the credibility and reliability of the MEDPRAT software.

Matthew T Prelich↗