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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Human Interfaces and Management of Information (HIMI) Challenges for “In-time” Aviation Safety Management Systems (IASMS)

The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.

Lawrence J Prinzel↗
NASA NTRS2022

Using Atmospheric Trace Gas Vertical Profiles to Evaluate Model Fluxes: A Case Study of Arctic-Cap Observations and Geos Simulations for the ABoVE Domain

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA’s Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations

CO2↗
NASA NTRS1974

Parameter estimation for terrain modeling from gradient data

This paper developes a method for mathematically modeling terrain surfaces for use on an unmanned Martian vehicle. The data collected by the vehicle consists of terrain height and two directional slopes at each data point. The parameters for the mathematical terrain model are stochastically determined by using least square approximations.-

Shen, C. N.↗
NASA NTRS1994

Issues concerning the updating of finite-element models from experimental data

Some issues concerning the updating of dynamic finite-element models by incorporation of experimental data are examined here. It is demonstrated how the number of unknowns can be greatly reduced if the physical nature of the model is maintained. The issue of uniqueness is also examined and it is shown that a number of previous workers have been mistaken in their attempts to define both sufficient and necessary measurement requirements for the updating problem to be solved uniquely. The relative merits of modal and frequency response function (frf) data are discussed and it is shown that for measurements at fewer degrees of freedom than are present in the model, frf data will be unlikely to converge easily to a solution. It is then examined how such problems may become more tractable by using new experimental techniques which would allow measurements at all degrees of freedom present in the mathematical model.

Dunn, Shane A.↗
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

Recent technology products from Space Human Factors research

The goals of the NASA Space Human Factors program and the research carried out concerning human factors are discussed with emphasis given to the development of human performance models, data, and tools. The major products from this program are described, which include the Laser Anthropometric Mapping System; a model of the human body for evaluating the kinematics and dynamics of human motion and strength in microgravity environment; an operational experience data base for verifying and validating the data repository of manned space flights; the Operational Experience Database Taxonomy; and a human-computer interaction laboratory whose products are the display softaware and requirements and the guideline documents and standards for applications on human-computer interaction. Special attention is given to the 'Convoltron', a prototype version of a signal processor for synthesizing the head-related transfer functions.

Jenkins, James P.↗
DOE OSTI2020

Nonlinear Logistic Regression Mixture Experiment Modeling for Binary Data Using Dimensionally Reduced Components

This article presents and illustrates an approach using nonlinear logistic regression for modeling binary response data from a mixture experiment when the components can be partitioned into groups used to form dimensionally-reduced pseudocomponents (DRPs). A DRP is a linear combination of the components in a group, where the linear combinations over all groups are normalized so that the DRP proportions sum to unity. Nonlinear logistic regression is required because, after normalization of the linear combinations, the model expressed in terms of the DRPs is nonlinear in the parameters that specify the linear combinations. A method for obtaining nonparametric tolerance limits on the probability of a “success” for the binary response variable using a bootstrap approach is also presented. Having three DRPs enables viewing data and modeling results on a ternary plot even though there may be many more than three mixture components. A real database, involving whether or not nepheline crystals form in simulated nuclear waste glass after cooling, is used to illustrate the nonlinear logistic regression modeling and nonparametric tolerance limit approaches when there are three DRPs.

Waste glass, Nepheline, Mixture experiment, Logist↗
DOE OSTI2023

Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast

Here, this paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of -0.14 m and -0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

LowFive v1.0

LowFive is a new data transport layer based on the HDF5 data model, for in situ workflows. Executables using LowFive can communicate in situ (using in-memory data and MPI message passing), reading and writing traditional HDF5 files to physical storage, and combining the two modes. Minimal and often no source-code modification is needed for programs that already use HDF5. LowFive maintains deep copies or shallow references of datasets, configurable by the user. More than one task can produce (write) data, and more than one task can consume (read) data, accommodating fan-in and fan-out in the workflow task graph. LowFive supports data redistribution from n producer processes to m consumer processes.

Morozov, Dmitriy↗
DOE OSTI2026

wa-hls4ml-paper

Code for plots, models, data generation and other utilities relating to the paper "wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation" https://arxiv.org/abs/2511.05615 [FERMILAB-PUB-25-0359-CSAID]

Hawks, Ben [Fermi National Accelerator Laboratory ↗
DOE OSTI2020

Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers

Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance.

Liu, Jeremy (ORCID:0000000172299417)↗
NASA NTRS1989

LOX/Hydrocarbon Combustion Instability Investigation

The LOX/Hydrocarbon Combustion Instability Investigation Program was structured to determine if the use of light hydrocarbon combustion fuels with liquid oxygen (LOX) produces combustion performance and stability behavior similar to the LOX/hydrogen propellant combination. In particular methane was investigated to determine if that fuel can be rated for combustion instability using the same techniques as previously used for LOX/hydrogen. These techniques included fuel temperature ramping and stability bomb tests. The hot fire program probed the combustion behavior of methane from ambient to subambient temperatures. Very interesting results were obtained from this program that have potential importance to future LOX/methane development programs. A very thorough and carefully reasoned documentation of the experimental data obtained is contained. The hot fire test logic and the associated tests are discussed. Subscale performance and stability rating testing was accomplished using 40,000 lb. thrust class hardware. Stability rating tests used both bombs and fuel temperature ramping techniques. The test program was successful in generating data for the evaluation of the methane stability characteristics relative to hydrogen and to anchor stability models. Data correlations, performance analysis, stability analyses, and key stability margin enhancement parameters are discussed.

Jensen, R. J.↗
NASA NTRS1989

Jet streak circulations associated with a moderate snowfall event as diagnosed from NGM model output

The existence, evolution, and interaction of vertical circulations associated with upper-level jet streaks during a moderate snowfall event are diagnosed using a nested grid model (NGM). The event itself is overviewed, and the diagnosis of transverse circulations utilizing NGM output is discussed. Focus is placed on the identification of the horizontal and vertical components of the circulation, the temporal evolution of the circulation, and the comparison of the circulation from successive model forecasts. A preliminary analysis of the model data indicates that the horizontal and vertical branches of the circulations can be diagnosed from the model output, and the horizontal and vertical components of the circulations may be identified and followed during an individual forecast cycle.

Kocin, Paul J.↗
NASA NTRS1992

System of Experts for Intelligent Data Management (SEIDAM)

It is proposed to conduct research and development on a system of expert systems for intelligent data management (SEIDAM). CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. At the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.

Goodenough, David G.↗
NASA NTRS1993

System of experts for intelligent data management (SEIDAM)

A proposal to conduct research and development on a system of expert systems for intelligent data management (SEIDAM) is being developed. CCRS has much expertise in developing systems for integrating geographic information with space and aircraft remote sensing data and in managing large archives of remotely sensed data. SEIDAM will be composed of expert systems grouped in three levels. At the lowest level, the expert systems will manage and integrate data from diverse sources, taking account of symbolic representation differences and varying accuracies. Existing software can be controlled by these expert systems, without rewriting existing software into an Artificial Intelligence (AI) language. At the second level, SEIDAM will take the interpreted data (symbolic and numerical) and combine these with data models. at the top level, SEIDAM will respond to user goals for predictive outcomes given existing data. The SEIDAM Project will address the research areas of expert systems, data management, storage and retrieval, and user access and interfaces.

Goodenough, David G.↗
NASA NTRS2008

Launch and Landing Effects Ground Operations (LLEGO) Model

LLEGO is a model for understanding recurring launch and landing operations costs at Kennedy Space Center for human space flight. Launch and landing operations are often referred to as ground processing, or ground operations. Currently, this function is specific to the ground operations for the Space Shuttle Space Transportation System within the Space Shuttle Program. The Constellation system to follow the Space Shuttle consists of the crewed Orion spacecraft atop an Ares I launch vehicle and the uncrewed Ares V cargo launch vehicle. The Constellation flight and ground systems build upon many elements of the existing Shuttle flight and ground hardware, as well as upon existing organizations and processes. In turn, the LLEGO model builds upon past ground operations research, modeling, data, and experience in estimating for future programs. Rather than to simply provide estimates, the LLEGO model s main purpose is to improve expenses by relating complex relationships among functions (ground operations contractor, subcontractors, civil service technical, center management, operations, etc.) to tangible drivers. Drivers include flight system complexity and reliability, as well as operations and supply chain management processes and technology. Together these factors define the operability and potential improvements for any future system, from the most direct to the least direct expenses.

Source record↗
NASA NTRS2009

Anvil Forecast Tool in the Advanced Weather Interactive Processing System

Meteorologists from the 45th Weather Squadron (45 WS) and National Weather Service Spaceflight Meteorology Group (SMG) have identified anvil forecasting as one of their most challenging tasks when predicting the probability of violations of the Lightning Launch Commit Criteria and Space Shuttle Flight Rules. As a result, the Applied Meteorology Unit (AMU) was tasked to create a graphical overlay tool for the Meteorological Interactive Data Display System (MIDDS) that indicates the threat of thunderstorm anvil clouds, using either observed or model forecast winds as input. The tool creates a graphic depicting the potential location of thunderstorm anvils one, two, and three hours into the future. The locations are based on the average of the upper level observed or forecasted winds. The graphic includes 10 and 20 n mi standoff circles centered at the location of interest, as well as one-, two-, and three-hour arcs in the upwind direction. The arcs extend outward across a 30 sector width based on a previous AMU study that determined thunderstorm anvils move in a direction plus or minus 15 of the upper-level wind direction. The AMU was then tasked to transition the tool to the Advanced Weather Interactive Processing System (AWIPS). SMG later requested the tool be updated to provide more flexibility and quicker access to model data. This presentation describes the work performed by the AMU to transition the tool into AWIPS, as well as the subsequent improvements made to the tool.

Barrett, Joe H., III↗
NASA NTRS1998

Fitting a Two-Component Scattering Model to Polarimetric SAR Data

Classification, decomposition and modeling of polarimetric SAR data has received a great deal of attention in the recent literature. The objective behind these efforts is to better understand the scattering mechanisms which give rise to the polarimetric signatures seen in SAR image data. In this Paper an approach is described, which involves the fit of a combination of two simple scattering mechanisms to polarimetric SAR observations. The mechanisms am canopy scatter from a cloud of randomly oriented oblate spheroids, and a ground scatter term, which can represent double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants or Bragg scatter from a moderately rough surface, seen through a layer of vertically oriented scatterers. An advantage of this model fit approach is that the scattering contributions from the two basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. The solution involves the estimation of four parameters from four separate equations. The model fit can be applied to polarimetric AIRSAR data at C-, L- and P-Band.

Freeman, A.↗