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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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At least 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Semi-automated Design of Artificial Intelligence Earth Science Models

Prediction and observation of water cycles involve not only patterns isolated in space and time, but rather modeling complex spatio-temporal relationships across multiple sources of data and domains. For instance, Evapotranspiration (ET) and Leaf Area Indexes (LAI) are two critical components in DOE’s Energy Exascale Earth System Model (E3SM). Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, current ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFT), which fail to capture the fine-scale variations due to change in vegetation type across the globe. Within this context and in light of the data-model integration challenges highlighted in the EESSD Strategic Plan, the new era of AI model development for geosciences calls for data-driven methods that provide domain scientists with estimations of parameters such as PFT and LAI in an efficient, interpretable, and easy-to-operate manner.

Ambrozio Dias, Philipe↗
DOE OSTI2015

An Analysis Platform for Multiscale Hydrogeologic Modeling with Emphasis on Hybrid Multiscale Methods

One of the most significant challenges facing hydrogeologic modelers is the disparity between those spatial and temporal scales at which fundamental flow, transport and reaction processes can best be understood and quantified (e.g., microscopic to pore scales, seconds to days) and those at which practical model predictions are needed (e.g., plume to aquifer scales, years to centuries). While the multiscale nature of hydrogeologic problems is widely recognized, technological limitations in computational and characterization restrict most practical modeling efforts to fairly coarse representations of heterogeneous properties and processes. For some modern problems, the necessary level of simplification is such that model parameters may lose physical meaning and model predictive ability is questionable for any conditions other than those to which the model was calibrated. Recently, there has been broad interest across a wide range of scientific and engineering disciplines in simulation approaches that more rigorously account for the multiscale nature of systems of interest. In this paper, we review a number of such approaches and propose a classification scheme for defining different types of multiscale simulation methods and those classes of problems to which they are most applicable. Our classification scheme is presented in terms of a flow chart (Multiscale Analysis Platform or MAP), and defines several different motifs of multiscale simulation. Within each motif, the member methods are reviewed and example applications are discussed. We focus attention on hybrid multiscale methods, in which two or more models with different physics described at fundamentally different scales are directly coupled within a single simulation. Very recently these methods have begun to be applied to groundwater flow and transport simulations, and we discuss these applications in the context of our classification scheme. As computational and characterization capabilities continue to improve, we envision that hybrid multiscale modeling will become more common and may become a viable alternative to conventional single-scale models in the near future.

Environmental Molecular Sciences Laboratory↗
NASA NTRS2004

Jet Noise Modeling for Supersonic Business Jet Application

This document describes the development of an improved predictive model for coannular jet noise, including noise suppression modifications applicable to small supersonic-cruise aircraft such as the Supersonic Business Jet (SBJ), for NASA Langley Research Center (LaRC). For such aircraft a wide range of propulsion and integration options are under consideration. Thus there is a need for very versatile design tools, including a noise prediction model. The approach used is similar to that used with great success by the Modern Technologies Corporation (MTC) in developing a noise prediction model for two-dimensional mixer ejector (2DME) nozzles under the High Speed Research Program and in developing a more recent model for coannular nozzles over a wide range of conditions. If highly suppressed configurations are ultimately required, the 2DME model is expected to provide reasonable prediction for these smaller scales, although this has not been demonstrated. It is considered likely that more modest suppression approaches, such as dual stream nozzles featuring chevron or chute suppressors, perhaps in conjunction with inverted velocity profiles (IVP), will be sufficient for the SBJ.

Stone, James R.↗
NASA NTRS2021

Application of a Developmental Composite Material Model to Predict the Crush Response of Two Energy Absorbers

In 2012, a consortium was formed with the goal of creating a new composite material model capable of predicting the wide range of properties, accumulated damage behavior, and the many different types of failure in composites under impact loading. The material model was developed for execution in the commercially available nonlinear, explicit transient dynamic finite element code, LS-DYNA®. This material model incorporates three sub-models: deformation, damage, and failure. In addition, the model accounts for strain rate and temperature effects and relies heavily on the input of tabulated material response data. The model is designated *MAT_COMPOSITE_TABULATED_PLASTICITY_DAMAGE, or *MAT_213. Initially, *MAT_213 was developed for use with solid elements only; however, a thin shell element formulation for *MAT_213 has been adapted recently. The objective of this project was to find a suitable modeling example to investigate the capabilities and performance of *MAT_213. In 2012, two composite energy absorbers were designed and evaluated at NASA Langley Research Center through multi-level testing and simulation. The first was a conical-shaped energy absorber, designated the conusoid, which consisted of four layers of hybrid carbon-Kevlar® plain-weave fabric oriented at [+45°/-45°/-45°/+45°] with respect to the vertical direction. The second was a sinusoidal-shaped energy absorber, designated the sinusoid, which consisted of hybrid carbon-Kevlar® plain-weave fabric face sheets, two layers for each face sheet oriented at ±45° with respect to the vertical direction, and a closed-cell ELFOAM® P200 polyisocyanurate foam core. Finite element models were developed of the energy absorbers and simulations were performed using LS-DYNA®. In this paper, the development of a *MAT_213 model of a hybrid carbon-Kevlar® plain-weave fabric is presented. Next, comparisons with material characterization tests are presented. Then, test-analysis results are documented for each energy absorber as comparisons of time-history responses, as well as predicted and experimental structural deformations and progressive damage under impact loading using the *MAT_213 material model. Since a prior *MAT_58, or *MAT_LAMINATED_COMPOSITE_FABRIC material model was used in previous simulations of the energy absorbers, comparisons are made between *MAT_58 and *MAT_213 model predictions with test data. Finally, the paper includes a comprehensive list of “lessons learned,” in which a series of parametric studies are documented that were performed to investigate specific issues related to the material model. These “lessons learned” are included in hopes that they may help future *MAT_213 users.

crashworthiness, LS-DYNA transient dynamic simulat↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
DOE OSTI2023

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. Here, this study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. Here, the model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter

A semi-empirical model is described for predicting unsteady aerodynamic forces on arbitrary airfoils under mildly stalled and unstalled conditions. Aerodynamic forces are modeled using second order ordinary differential equations for lift and moment with airfoil motion as the input. This model is simultaneously integrated with structural dynamics equations to determine flutter characteristics for a two degrees-of-freedom system. Results for a number of cases are presented to demonstrate the suitability of this model to predict flutter. Comparison is made to the flutter characteristics determined by a Navier-Stokes solver and also the classical incompressible potential flow theory.

Mahajan, Aparajit J.↗
NASA NTRS1993

Semi-empirical model for prediction of unsteady forces on an airfoil with application to flutter

A semi-empirical model is described for predicting unsteady aerodynamic forces on arbitrary airfoils under mildly stalled and unstalled conditions. Aerodynamic forces are modeled using second order ordinary differential equations for lift and moment with airfoil motion as the input. This model is simultaneously integrated with structural dynamics equations to determine flutter characteristics for a two degrees-of-freedom system. Results for a number of cases are presented to demonstrate the suitability of this model to predict flutter. Comparison is made to the flutter characteristics determined by a Navier-Stokes solver and also the classical incompressible potential flow theory.

Mahajan, A. J.↗
DOE OSTI2021

Semi-automated Design of Artificial Intelligence Earth Systems Models

Prediction and observation of water cycles at various scales involve not only patterns isolated in space and time, but also modeling of complex spatio-temporal relationships across multiple domains. For instance, evapotranspiration (ET) and leaf area indexes (LAI) are two parameters that are needed to accurately model and understand land-atmosphere processes. Accurate assessments of ET and LAI are critical for understanding hydrological processes, deforestation, crop yield, and irrigation impacts. However, ET estimates for global simulations are available at very coarse spatial resolution. They are usually derived from satellite data based on broad plant functional types (PFTs), which fail to capture fine-scale variations because of changes in vegetation type across the globe. Similarly LAI estimates have typically been derived from vegetation indices at global scales or estimated locally using physical models, both of which suffer from a range of uncertainties that impact model sensitivity. The new era of AI model development for Earth systems (ES) calls for data-driven methods that provide domain scientists with uncertainty-aware estimations of biophysical parameters such as ET and LAI in a generalizable, interpretable, and discoverable manner.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

The use of computer models to predict temperature and smoke movement in high bay spaces

The Building and Fire Research Laboratory (BFRL) was given the opportunity to make measurements during fire calibration tests of the heat detection system in an aircraft hangar with a nominal 30.4 (100 ft) ceiling height near Dallas, TX. Fire gas temperatures resulting from an approximately 8250 kW isopropyl alcohol pool fire were measured above the fire and along the ceiling. The results of the experiments were then compared to predictions from the computer fire models DETACT-QS, FPETOOL, and LAVENT. In section A of the analysis conducted, DETACT-QS AND FPETOOL significantly underpredicted the gas temperature. LAVENT at the position below the ceiling corresponding to maximum temperature and velocity provided better agreement with the data. For large spaces, hot gas transport time and an improved fire plume dynamics model should be incorporated into the computer fire model activation routines. A computational fluid dynamics (CFD) model, HARWELL FLOW3D, was then used to model the hot gas movement in the space. Reasonable agreement was found between the temperatures predicted from the CFD calculations and the temperatures measured in the aircraft hangar. In section B, an existing NASA high bay space was modeled using the CFD model. The NASA space was a clean room, 27.4 m (90 ft) high with forced horizontal laminar flow. The purpose of this analysis is to determine how the existing fire detection devices would respond to various size fires in the space. The analysis was conducted for 32 MW, 400 kW, and 40 kW fires.

Notarianni, Kathy A.↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗
NASA NTRSDate not supplied

Modeling of a Solid Oxide Fuel Cell as Part of a Predictive Functional Model for Aerospace Fuel-Cell Systems

This paper will discuss initial efforts at modeling a solid oxide fuel cell (SOFC) for aerospace applications. Fuel cells historically have been used in spacecraft from the Gemini to the Shuttle era for providing spacecraft power with the added benefit of producing water for crew use. However, there are many potential applications for fuel cells and electrolyzers in spaceflight, including oxygen generation, in-situ resource utilization (ISRU) and propellant production. This proof-of-concept model has been developed using a commercial multiphysics modeling software package. Two-dimensional and one-dimensional isothermal models were created based on a certain SOFC design and results were compared to test data from the real system. Local Butler-Volmer kinetic relations were adjusted, and an effective porous medium approach was taken in order to capture how the many interconnects between cells in the gas channels affected fluid flow. The model was able to reproduce polarization curves derived from test data within around 0.02 Volts for a given current density. This model could be adapted to model a solid oxide electrolyzer, proton exchange membrane fuel cell, or other type of fuel cell technology in order to understand how these types of technologies could fit into broader spacecraft designs and advance the capabilities of spaceflight systems.

Mary Lou Nadeau↗