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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Artificial Neural Network Modeling for Airline Disruption Management

Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.

Kolawole Ogunsina↗
NASA NTRSDate not supplied

A Machine Learning Approach to Determine Surface Radiative Fluxes based on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) projects provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. An alternative data product, Fast Longwave and Shortwave radiative Flux (FLASHFlux), was created to provide data to the applied sciences and educational users. FLASHFlux provides Top-of-Atmosphere radiative fluxes, Clouds properties, and parameterized surface radiative fluxes within four days for footprint (Level 2) data. We investigate the use of Artificial Neural Network (ANN) using MODerate resolution Imaging Spectroradiometer (MODIS) derived clouds properties and meteorology from the Global Assimilation and Meteorology Office (GMAO) scaled to the CERES footprint from the CERES Clouds Radiative Swath (CRS) data product to compute surface radiative fluxes. We test ANN produce fluxes against surface fluxes produced from the Fu-Liou model used in CRS and the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) used in FLASHFlux. We also validated each model with ground-based observations. Furthermore, we investigate Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training and provide insight for future models. Advances in machine learning, along with increases in computational capabilities and available data allow us to estimate effects of unresolved processes in our climate without direct modeling. This work evaluates the ability to create accurate data-driven models to supplement or replace current models that estimate surface radiative fluxes.

Climatology↗
NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗
DOE OSTI2021

Data-driven estimation of energy consumption for electric bus under real-world driving conditions

Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.

Artificial neural network↗
DOE OSTI2022

A Machine Learning-Assisted Framework to Control Thermally Anisotropic Building Envelopes in Residential Buildings

To curb the energy consumption of buildings and their related CO2 emissions, Oak Ridge National Laboratory (ORNL) has developed the thermally anisotropic building envelope (TABE) —a multi-layer design comprising insulation materials and metal foils connected to thermal loops. In this study, a machine learning-assisted framework was developed to control the TABE in residential buildings to reduce the computation load for future optimal rule-based control and application. First, a 2D finite element model was established in COMSOL to calculate the hourly heat flux through exterior walls installed with the TABE. Then, TABE wall heat fluxes were simulated for various given indoor and outdoor boundary conditions, as well as thermal loops fluid temperatures and flow rates. Since the finite element simulations are computationally expensive, an artificial neural network (ANN) was then trained to use as a proxy of the finite element (COMSOL) modeling. Finally, the trained ANN model was coupled with the EnergyPlus model to predict the energy consumption of a US Department of Energy prototype single-family house installed with the TABE. An optimal simple rule-based control was determined from predefined rules for a case study. The results demonstrate that the developed machine learning–assisted framework can reduce 99.9% of the computation time while efficiently managing residential building energy for installed TABE walls.

Shen, Zhenglai↗
DOE OSTI2021

Nonlinear Multiple Models Adaptive Secondary Voltage Control of Microgrids

This article proposes a model-free secondary voltage control (SVC) for microgrids (MG) using nonlinear multiple models adaptive control. Firstly, a linear robust adaptive controller is designed to guarantee the voltage stability in the bounded-input- bounded-output (BIBO) manner so as to meet the operation requirements of MGs. Secondly, a nonlinear adaptive controller is developed to improve the voltage tracking performance with the help of artificial neural networks (ANNs). A switching mechanism for coordinating such two controllers is designed to guarantee the closed-loop stability while achieving accurate voltage tracking. Here, by an online identification based on the input and output data of MGs, the proposed method does not resort to any apriori information of system model and primary control, thus exhibiting good robustness, ease of deployment and disturbance rejection.

17 WIND ENERGY↗
DOE OSTI2025

Correction: “When I talk about it, my eyes light up!” Impacts of a national laboratory internship on community college student success

The ORCID iDs are missing for the second, fourth, sixth, seventh, eighth and ninth author. Please see the authors’ respective ORCID iDs here: Author Seth Van Doren’s ORCID iD is: 0000-0003-0674-277X (https://orcid.org/0000-0003-0674-277X). Author Julio Jaramillo Salcido’s ORCID iD is: 0000-0002-4113-5345 (https://orcid.org/orcid.org/0000-0002-4113-5345) Author Gabriel Otero Munoz’s ORCID iD is: 0000-0002-1444-8020 (https://orcid.org/0000-0002-1444-8020) Author Aparna Manocha’s ORCID iD is: 0000-0001-7824-9971 (https://orcid.org/0000-0001-7824-9971) Author Colette L. Flood’s ORCID iD is: 0000-0002-8674-0872 (https://orcid.org/0000-0002-8674-0872) Author Anne M. Baranger’s ORCID iD is: 0000-0002-1973-4632 (https://orcid.org/0000-0002-1973-4632)

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Machine Learning Model Geotiffs - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains geotiffs, supporting shapefiles and readmes for the inputs and output models of algorithms explored in the Nevada Geothermal Machine Learning project, meant to accompany the final report. Layers include: Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk), input rasters of feature sets, and positive/negative training sites. See readme .txt files and final report for additional metadata. A submission linking the full codebase for generating machine learning output models is available under "related resources" on this page.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

GIS Resource Compilation Map Package - Applications of Machine Learning Techniques to Geothermal Play Fairway Analysis in the Great Basin Region, Nevada

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Distribution Integration Solution Cost Options (DISCO) [Slides]

DISCO is a Python-based NREL software tool for conducting scalable, repeatable distribution analyses. Although DISCO was originally developed to support photovoltaic impact analyses, it can also be used to understand the impact of other distributed energy resources and load changes on distribution systems. This presentation took place on April 9, 2024, and included NREL power grid researchers Sherin Ann Abraham and Shibani Ghosh.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2021

Fine-tuning Polymers as Flexible Antenna Substrates for Aircraft

The pursuit to design more lightweight aircraft that can travel over greater distances never ceases. One area that has recieved much attention is the development of lighter and more aerodynamic antennas. Researchers at the NASA Glenn Research Center at the forefront of this effort. The research team, which includes Jessica Cashman, Baochau Nguyen, Haiquan Guo, Marcos Pantoja, Kevin Cavicchi, and Mary Ann Meador, is designing flexible polymer materials that can be used to construct lightweight, low-drag antennas that lie along the surface of an aircraft.

Haiquan Guo↗
DOE OSTI2020

Panel Session 94: US DOE Procurement Opportunities and End-State Contracting Model Status

This panel focused on DoE's procurement implementation strategy and planned activities for its first-tier contractors (Tier 1 procurements). This panel also focused on the implementation status of DoE's End State Contracting Model, which uses a single-award ID/IQ approach to give DOE flexibility to better define discrete scopes of work for site closure or end states. The panelists provided their prospective on progress toward the implementation of this new approach, lessons learned and the path forward for upcoming opportunities utilizing this contracting model. Panelists with presentations: EFCOG (Billy Morrison); DOE Procurement Opportunities - 4 Ways for Small Business Participation (Anne Marie Bird); US DOE Procurement and Contracting Opportunities (Aaron Deckard)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Face‐on Orientation Matches Vertical Organic Electrochemical Transistors for High Transconductance and Superior Non‐Volatility

Abstract The recently developed vertical structure of organic electrochemical transistors (OECTs) can integrate volatile and non‐volatile functions into one reconfigurable device, making it highly promising. However, comparing with the conventional planar OECT (c‐OECT), the understanding of vertical OECT (v‐OECT) working principles and device engineering strategies is still lacking, impeding rational optimization. Since a major difference between c‐ and v‐OECTs is their charge transport directionality, which is highly influenced by crystallite orientations, the orientation–device structure match thus becomes an important yet outstanding topic for OECTs. Herein, using ann‐type small molecule IDIC‐MEG, investigate how much impact such match can have on OECT performance. The IDIC‐MEG c‐OECT fails to work due to the seriously hindered in‐plane electron transport by face‐on orientation. Surprisingly, simply changing the device structure from planar to vertical allows the resultant v‐OECT to exhibit the highest reported transconductance (46.3 mS) among all small‐molecule OECTs, thanks to the match between face‐on orientation and the vertical structure. Such match also leads to excellent non‐volatility, including highly predictable programmability and good operational stability. This work, for the first time, explicitly demonstrates the significance of orientation–device structure match for OECT optimization, establishing new guidelines for achieving high‐performance volatile and non‐volatile OECTs.

Chemistry↗
DOE OSTI2025

Ultra-Fast Non-Volatile Resistive Switching Devices with Over 512 Distinct and Stable Levels for Memory and Neuromorphic Computing

Low-current multilevel programmability with inherent non-volatility and high stability of resistance states is required for both multi-bit memory storage and deep learning accelerators but is difficult to achieve. Here, in a resistive switching system, this work realizes >512 (>9 bits) distinct non-volatile conductance levels with stable retention for each state with current levels down to the nanoampere range, highly promising for potential integration with small processing nodes with ultra-low power consumption requirements. This is achieved by demonstrating a new thin film design concept that encompasses three key features: an ultra-thin epitaxial oxygen ionic switching layer that provides a tunable energy barrier at the bottom electrode, an overcoat amorphous layer that acts as an ion migration barrier for stable state retention, and a partial conductive filament as a localized electronic transport channel to the epitaxial switching layer. A large dynamic resistance range of up to seven orders of magnitude is achieved with reset-free transitions among intermediate states, and programmability is demonstrated with ultra-fast (20 ns) pulses. Artificial neural network (ANN) simulations, based on the experimental performance and its non-idealities, demonstrate close-to-ideal inference accuracies for various Modified National Institute of Standards and Technology (MNIST) data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Abstract Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali‐counterions can inform natural and synthetic aqueous processes. In the study of POMs ([Nb 24 O 72 H 9 ] 15− , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali‐POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ‐profile) and use an artificial neural network (ANN) to predict all three described alkali‐anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution‐phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14− , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

Rahman, Tasnim↗
DOE OSTI2022

Predicting the Solubility of Inorganic Ion Pairs in Water

Polyoxometalates (POMs), ranging in size from 1 to 10’s of nanometers, resemble building blocks of inorganic materials. Elucidating their complex solubility behavior with alkali-counterions can inform natural and synthetic aqueous processes. Here in the study of POMs ([Nb 24 O 72 H 9 ] 15- , Nb 24 ) we discovered an unusual solubility trend (termed anomalous solubility) of alkali-POMs, in which Nb 24 is most soluble with the smallest (Li + ) and largest (Rb/Cs + ) alkalis, and least soluble with Na/K + . Via computation, we define a descriptor (σ-profile) and use an artificial neural network (ANN) to predict all three described alkali-anion solubility trends: amphoteric, normal (Li + >Na + >K + >Rb + >Cs + ), and anomalous (Cs + >Rb + >K + >Na + >Li + ). Testing predicted amphoteric solubility affirmed the accuracy of the descriptor, provided solution-phase snapshots of alkali–POM interactions, yielded a new POM formulated [Ti 6 Nb 14 O 54 ] 14- , and provides guidelines to exploit alkali–POM interactions for new POMs discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗