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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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All sources NASA NTRS DOE OSTI

DOE OSTI2026

Outdoor Deployment Data for a Four-Terminal GaAs//Si Tandem Solar Mini-Module

This dataset contains the complete outdoor measurement and analysis data for a mechanically stacked, four-terminal (4T) gallium arsenide (GaAs)//silicon (Si) tandem solar mini-module deployed from October 2019 to January 2021 at the Solar Radiation Research Laboratory (SRRL) in Golden, Colorado, USA. The data support a performance modeling and degradation analysis framework for tandem photovoltaic devices, as described in the accompanying publication. The dataset includes: (1) current–voltage (J–V) characteristics of each sub-cell measured approximately every five minutes, with extracted performance parameters; (2) spectral irradiance from an EKO MS-710 WISER spectroradiometer, along with derived spectral mismatch ratios (SMR) and average photon energy (APE); (3) one-minute resolution meteorological data from the co-located SRRL weather station and GPS-derived precipitable water vapor (PWV); (4) pre-deployment laboratory characterization (external quantum efficiency, J–V curves, standard test conditions parameters); (5) outdoor-extracted temperature and PWV correction coefficients; and (6) PVcircuit equivalent-circuit simulation outputs used for model validation. Degradation rates of −4.1 ± 0.2 %/year (GaAs) and −2.5 ± 0.9 %/year (Si) were determined using a filtering and normalization methodology adapted for fixed-tilt tandem modules. All data are provided in open, portable formats (Apache Parquet, CSV, JSON) to enable full reproducibility of the published analysis.

14 SOLAR ENERGY↗

NASA NTRS2002

Human performance cognitive-behavioral modeling: a benefit for occupational safety

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Occupational Health↗

DOE OSTI2022

Battery Performance and Cost Modeling for Electric-Drive Vehicles (A Manual for BatPaC v5.0)

This manual details the fifth version of the Battery Performance and Cost (BatPaC v5.0) model developed at Argonne National Laboratory for lithium-ion battery packs used in transportation (file “BatPaC 5.0 2022-07-22.xlsm”). BatPaC is a publicly available model that performs a bottom-up lithium-ion battery design and cost calculation. The model designs the battery for a specified power, energy, and vehicle type (i.e., hybrid, plug-in hybrid, or full-electric). The cost of the designed battery is calculated by accounting for every step in the lithium-ion battery manufacturing process. The original model and manual were publicly peer-reviewed by battery experts assembled by the U.S. Environmental Protection Agency. This revised model and manual include changes made in response to comments received from users and the observed trajectory of the industry.

25 ENERGY STORAGE↗

DOE OSTI2026

VRN3P: Variational Recurrent Neural Network Based Net-Load Prediction under High Solar Penetration

This is the final technical report for the SETO-funded VRN3P project (PNNL# 76914). The goal of this project, led by Pacific Northwest National Laboratory (PNNL), in collaboration with Lawrence Livermore National Laboratory (LLNL) and Portland General Electric (PGE), was to develop and validate a deep variational recurrent neural network-based net-load prediction (VRN3P) framework for probabilistic time-series forecasting of day-ahead net-load under high solar penetration scenarios. The project team reports successful design of a novel probabilistic net-load forecasting architecture, comprising of a variational autoencoder and a recurrent neural network, which demonstrates 30% improvement in forecast performance, 60% improvement in training time, and consumes 44% less memory, when compared with conventional baseline models. The team tested the VRN3P model performance on GridLAB-D test-cases representing varying BTM solar penetration levels of 20%, 30%, and 50%, with integrated time-series net-load profiles provided by the utility partner (PGE). The VRN3P model demonstrate <2% hourly MAPE (averaged over the year) for day- ahead net-load forecast on the test scenario with 20% BTM solar. Transfer learning extension of the VRN3P model has demonstrated 8.33× speed-up in training, while still achieving acceptable forecast performance of 2.24% hourly MAPE on the 30% BTM solar penetration test-scenario. A preliminary version of the VRN3P GridAPPS-D™has been developed, along with a web-based interactive user-interface (named ‘Forte’) which has made available on GitHub for public use.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1972

Installation effects on performance of multiple model V/STOL lift fans

An experimental program was performed in which the individual performance of multiple VTOL model lift fans was measured. The model tested consisted of three 5.5 in. diameter tip-turbine driven model VTOL lift fans mounted chordwise in a two-dimensional wing to simulate a pod-type array. The performance data provided significant insight into possible thrust variations and losses caused by the presence of cover doors, adjacent fuselage panels, and adjacent fans. The effect of a partial loss of drive air supply (simulated gas generator failure) on fan performance was also investigated. The results of the tests demonstrated that lift fan installation variables and hardware can have a significant effect on the thrust of the individual fans.

Diedrich, J. H.↗

NASA NTRS2009

Proposed Schematics and Modeling Results for a Lunar Portable Life Support System

The Constellation Space Suit Element (CSSE) is an integrated assembly made up of primarily a Pressure Garment System (PGS) and a Portable Life Support System (PLSS). The PLSS is further composed of an oxygen (O2) subsystem, a ventilation subsystem, and a thermal subsystem. This paper baselines a detailed schematic of the CSSE PLSS to provide a basis for current and future CSSE PLSS development efforts. Both context diagrams and detailed schematics describe the hardware components and overall functions for all three of the PLSS subsystems. Additionally, PLSS functions are presented for multiple operational scenarios as follows: 1) Nominal Extravehicular Activity (EVA) Mode; 2) Umbilical Modes; a) No Recharge, b) With Recharge; 3) Decompression Sickness (DCS) Treatment Mode; 4) Buddy Mode; 5) Secondary O2 Modes; a) Helmet Purge; b) Suit Purge; c) Operational; and 5) PLSS Removed Umbilical Mode. A performance modeling effort is being performed to provide a preliminary confirmation of this layout and the current state of the thermal hydraulic modeling efforts being conducted for the PLSS is presented. The goal of these efforts is to provide realistic simulations of the PLSS under various modes of operation. Modeling approaches and assumptions are discussed as well as component model descriptions. Results from the models are included that show PLSS operations at steady-state and transient conditions. Finally, conclusions and recommendations are offered that summarize results, identify PLSS design weaknesses uncovered during review of the analysis results, and propose areas for improvement to increase model fidelity and accuracy.

Conger, Bruce↗

NASA NTRS1986

Study of selected tether applications in space, phase 3, volume 2

The results of a Phase 3 study of two Selected Tether Applications in Space (STAIS); deorbit of a Shuttle and launch of an Orbital Transfer Vehicle (OTV), both from the space station using a tether were examined. The study objectives were to: perform a preliminary engineering design, define operational scenarios, develop a common cost model, perform cost benefits analyses, and develop a Work Breakdown Structure (WBS). Key features of the performance analysis were to identify the net increases in effective Shuttle cargo capability if tethers are used to assist in the deorbit of Shuttles and the launching of the OTVs from the space station and to define deployer system designs required to accomplish these tasks. Deployer concepts were designed and discussed. Operational scenarios, including timelines, for both tethered and nontethered Shuttle and OTV operations at the space station were evaluated. A summary discussion of the Selected Tether Applications Cost Model (STACOM) and the results of the cost benefits analysis are presented. Several critical technologies needed to implement tether assisted deployment of payloads are also discussed. Conclusions and recommendations are presented.

Source record↗

NASA NTRS2016

Performance Metrics, Error Modeling, and Uncertainty Quantification

A common set of statistical metrics has been used to summarize the performance of models or measurements- the most widely used ones being bias, mean square error, and linear correlation coefficient. They assume linear, additive, Gaussian errors, and they are interdependent, incomplete, and incapable of directly quantifying uncertainty. The authors demonstrate that these metrics can be directly derived from the parameters of the simple linear error model. Since a correct error model captures the full error information, it is argued that the specification of a parametric error model should be an alternative to the metrics-based approach. The error-modeling methodology is applicable to both linear and nonlinear errors, while the metrics are only meaningful for linear errors. In addition, the error model expresses the error structure more naturally, and directly quantifies uncertainty. This argument is further explained by highlighting the intrinsic connections between the performance metrics, the error model, and the joint distribution between the data and the reference.

Quantification↗

NASA NTRS1991

On-orbit structural dynamic performance of a low-frequency microwave radiometer for Mission to Planet Earth applications

The present paper addresses the on-orbit dynamic performance of a low-frequency microwave radiometer for earth science applications. The radiometer is one of the earth-observing instruments aboard a geostationary platform proposed as part of the Mission to Planet Earth. The paper includes establishing the structural requirements of the antenna, developing the structural and disturbance models, performing modal and forced response analyses, and evaluating the resulting distortions in terms of the antenna's ability to meet stringent structural performance requirements. Two antenna configurations are discussed: free-flying and platform-mounted. These configurations are analyzed for a representative disturbance function which simulates rotation of the subreflector in order to perform a raster-type scan of the earth disk. Results show that the scanning maneuver modeled did not induce antenna performance errors which were outside their estimated limits.

Wahls, Deborah M.↗

DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty-animal model.” Our species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91%–94% on out-of-sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Improving E3SM Land Model Photosynthesis Parameterization via Satellite SIF, Machine Learning, and Surrogate Modeling

The parameterization of key photosynthesis parameters is one of the key uncertain sources in modeling ecosystem gross primary productivity (GPP). Solar-induced chlorophyll fluorescence (SIF) offers a good proxy for GPP since it marks the actual process of photosynthesis; while machine learning (ML) provides a robust approach to model the GPP-SIF relationship. Here, we trained the boosted regressing tree (BRT) and the Random Forest ML models with Greenhouse Gases Observing Satellite SIF data and in situ GPP observations from 49 eddy covariance towers. These trained ML GPP-SIF models were fed into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to generate ELM-simulated global SIF estimates, which were then benchmarked against satellite SIF observations with a surrogate modeling approach. Our results indicated good modeling performance of the ML-based GPP-SIF relationship. The ELM model when fed with the ML GPP-SIF models also can well predict the spatial-temporal variations in SIF. We also found high model accuracy for the surrogate modeling. Model parameter sensitivity analysis suggested that the fraction of leaf nitrogen in RuBisCO (flnr) is the most sensitive parameter to the SIF; other sensitive parameters include the Ball-Berry stomatal conductance slope (mbbopt) and the vcmax entropy (vcmaxse). The posterior uncertainty in simulated GPP was greatly reduced after benchmarking, and the model produced improved spatial patterns of mean GPP relative to FLUXCOM GPP. Our integrated approach provides a new avenue for improving land models and using remote-sensing SIF, which can be further improved in the future with more ground- and satellite-based observations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

CovTransformer: A transformer model for SARS-CoV-2 lineage frequency forecasting

With hundreds of SARS-CoV-2 lineages circulating in the global population, there is an ongoing need for predicting and forecasting lineage frequencies and thus identifying rapidly expanding lineages. Accurate prediction would allow for more focused experimental efforts to understand pathogenicity of future dominating lineages and characterize the extent of their immune escape. Here, we first show that the inherent noise and biases in lineage frequency data make a commonly-used regression-based approach unreliable. To address this weakness, we constructed a machine learning model for SARS-CoV-2 lineage frequency forecasting, called CovTransformer, based on the transformer architecture. We designed our model to navigate challenges such as a limited amount of data with high levels of noise and bias. We first trained and tested the model using data from the UK and the USA, and then tested the generalization ability of the model to many other countries and US states. Remarkably, the trained model makes accurate predictions two months into the future with high levels of accuracy both globally (in 31 countries with high levels of sequencing effort) and at the US-state level. Our model performed substantially better than a widely used forecasting tool, the multinomial regression model implemented in Nextstrain, demonstrating its utility in SARS-CoV-2 monitoring. Assuming a newly emerged lineage is identified and assigned, our test using retrospective data shows that our model is able to identify the dominating lineages 7 weeks in advance on average before they became dominant. Overall, our work demonstrates that transformer models represent a promising approach for SARS-CoV-2 forecasting and pandemic monitoring.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Importance of different parameterization changes for the updated dust cycle modeling in the Community Atmosphere Model (version 6.1)

Abstract. The Community Atmosphere Model (CAM6.1), the atmospheric component of the Community Earth System Model (CESM; version 2.1), simulates the life cycle (emission, transport, and deposition) of mineral dust and its interactions with physio-chemical components to quantify the impacts of dust on climate and the Earth system. The accuracy of such quantifications relies on how well dust-related processes are represented in the model. Here we update the parameterizations for the dust module, including those on the dust emission scheme, the aerosol dry deposition scheme, the size distribution of transported dust, and the treatment of dust particle shape. Multiple simulations were undertaken to evaluate the model performance against diverse observations, and to understand how each update alters the modeled dust cycle and the simulated dust direct radiative effect. The model–observation comparisons suggest that substantially improved model representations of the dust cycle are achieved primarily through the new more physically-based dust emission scheme. In comparison, the other modifications induced small changes to the modeled dust cycle and model–observation comparisons, except the size distribution of dust in the coarse mode, which can be even more influential than that of replacing the dust emission scheme. We highlight which changes introduced here are important for which regions, shedding light on further dust model developments required for more accurately estimating interactions between dust and climate.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1977

Evaluation of turbulence models for three primary types of shock-separated boundary layers

Zero-equation (algebraic), one-equation (kinetic energy), and two-equation (kinetic energy plus length scale) turbulence eddy viscosity models were used in computing three basic types of shock-separated boundary-layer flows. The three basic types of shock boundary-layer interaction discussed are: (1) a normal shock wave at transonic speeds, (2) a compression corner shock at supersonic speeds, and (3) an incident oblique shock at hypersonic speeds. The models tested are simple, unmodified models used extensively for incompressible, unseparated flows. A comparison of computed and measured results for the compressible, separated flows described herein indicates that model performance is dependent on flow configuration with no distinct superiority of one model over the other for all three flow configurations.

Coakley, T. J.↗

NASA NTRS1986

The measurement of aircraft performance and stability and control after flight through natural icing conditions

The effects of airframe icing on the performance and stability and control of a twin-engine commuter-class aircraft were measured by the NASA Lewis Research Center. This work consisted of clear air tests with artificial ice shapes attached to the horizontal tail, and natural icing flight tests in measured icing clouds. The clear air tests employed static longitudinal flight test methods to determine degradation in stability margins for four simulated ice shapes. The natural icing flight tests employed a data acquisition system, which was provided under contract to NASA by Kohlman Systems Research Incorporated. This system used a performance modeling method and modified maximum likelihood estimation (MMLE) technique to determine aircraft performance degradation and stability and control. Flight test results with artificial ice shapes showed that longitudinal, stick-fixed, static margins are reduced on the order of 5 percent with flaps up. Natural icing tests with the KSR system corroborated these results and showed degradation in the elevator control derivatives on the order of 8 to 16 percent depending on wing flap configuration. Performance analyses showed the individual contributions of major airframe components to the overall degration in lift and drag.

Ranaudo, R. J.↗

NASA NTRS1986

The measurement of aircraft performance and stability and control after flight through natural icing conditions

Ranaudo, R. J.↗

NASA NTRS1989

Vector performance analysis of three supercomputers - Cray-2, Cray Y-MP, and ETA10-Q

Results are presented of a series of experiments to study the single-processor performance of three supercomputers: Cray-2, Cray Y-MP, and ETA10-Q. The main object of this study is to determine the impact of certain architectural features on the performance of modern supercomputers. Features such as clock period, memory links, memory organization, multiple functional units, and chaining are considered. A simple performance model is used to examine the impact of these features on the performance of a set of basic operations. The results of implementing this set on these machines for three vector lengths and three memory strides are presented and compared. For unit stride operations, the Cray Y-MP outperformed the Cray-2 by as much as three times and the ETA10-Q by as much as four times for these operations. Moreover, unlike the Cray-2 and ETA10-Q, even-numbered strides do not cause a major performance degradation on the Cray Y-MP. Two numerical algorithms are also used for comparison. For three problem sizes of both algorithms, the Cray Y-MP outperformed the Cray-2 by 43 percent to 68 percent and the ETA10-Q by four to eight times.

Fatoohi, Rod A.↗