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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 901 records · Page 50

Magnetotail Boundary Crossings at Lunar Distances: ARTEMIS Observations

We compare results from a preliminary analysis of two years of ARTEMIS magnetopause boundary crossings at lunar distances with available empirical models. We remove the effects of variable solar wind flow directions and aberration angles to study the magnetotail cross-section as a function of solar wind conditions. The average magnetopause distance from the central axis is 26 RE, but this distance ranges from 10 RE for high solar wind dynamic pressures and strong northward IMF orientations to 39 RE for low solar wind dynamic pressures and weak southward IMF orientations compared to the nominal solar wind conditions. The time-independent Howe and Binsack (1972) model describes the average location of the crossings very well. For high solar wind dynamic pressures, the Lu et al., (2011) model performs best, while for low pressures the Petrinec and Russell (1996) model gives the closest prediction. As predicted by theory and seen in past studies, the magnetotail cross-section is suggestive of prolate during intervals of strong IMF By, but oblate during intervals of strong IMF Bz. Any asymmetric variation of the tail boundary with respect to the sign of IMF By was not observed. The decreasing size of boundary with the increasing dynamic pressure was found when dynamic pressures are smaller than 2nPa. Although the scatter is larger, the tail size for pressures larger than 2nPa suggests a constant radius. The tail boundary size decreases as the strength of IMF Bz increases regardless of its polarity. However, it was also observed that an even stronger southward IMF Bz can cause larger magnetopause size in the presence of large dynamic pressures.

Magnetotail↗

Using Long-Short Term Memory Models to Predict Solar Active Regions Emergence

We train Long Short-Term Memory (LSTM) models that predict the formation of active regions (ARs), the main source of eruptive solar activity. Using the Doppler shift velocity, the continuum intensity and the magnetic field full-disk maps from SDO/HMI we have created time-series datasets of acoustic power and magnetic flux which are used to train Long Short-Term Memory (LSTM) models on predicting decreases in continuum intensity 12 hours in advance. Testing of the models' performance was done on data from 5 ARs, unseen from the model during training. The model predicted the emergence of AR11726, AR13165 and AR13179, 10, 29 and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet parts of the solar disc, showing the ability of the model to capture acoustic power anomalies and predict continuum intensity variations. This work sets the foundations for the very first ML-aided prediction of solar ARs.

SMD↗

Lightweight ZERODUR: Validation of Mirror Performance and Mirror Modeling Predictions

Upcoming spaceborne missions, both moderate and large in scale, require extreme dimensional stability while relying both upon established lightweight mirror materials, and also upon accurate modeling methods to predict performance under varying boundary conditions. We describe tests, recently performed at NASA's XRCF chambers and laboratories in Huntsville Alabama, during which a 1.2 m diameter, f/1.2988% lightweighted SCHOTT lightweighted ZERODUR(TradeMark) mirror was tested for thermal stability under static loads in steps down to 230K. Test results are compared to model predictions, based upon recently published data on ZERODUR(TradeMark). In addition to monitoring the mirror surface for thermal perturbations in XRCF Thermal Vacuum tests, static load gravity deformations have been measured and compared to model predictions. Also the Modal Response(dynamic disturbance) was measured and compared to model. We will discuss the fabrication approach and optomechanical design of the ZERODUR(TradeMark) mirror substrate by SCHOTT, its optical preparation for test by Arizona Optical Systems (AOS). Summarize the outcome of NASA's XRCF tests and model validations

technology↗

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗

Validation of Universal Cryogenic Flow Boiling Correlations in Thermal Desktop for Liquid Hydrogen

Developing accurate models of two-phase cryogenic flow will reduce risk and reduce margins for future NASA vehicles such as the Nuclear Thermal Propulsion (NTP) system. Currently there is a need for more accurate, direct cryogenic data-anchored models for various boiling and two-phase phenomena. The focus of the current work is on modeling steady state cryogenic flow boiling in the transfer line that connects a propellant storage tank to an engine or customer receiver tank. This paper presents Thermal Desktop model validation results against several historical liquid hydrogen (LH2) heated tube experiments that cover a wide range of inlet conditions, mass flux, and heat flux. New universal cryogenic flow boiling correlations were recently developed and anchored to the largest cryogenic flow boiling database assembled in the world to-date. The new universal correlations are patched together and implemented into Thermal Desktop to compare model performance against the built-in flow boiling correlations. Axial wall temperature predictions as a function of preponderant parameters like heat flux, mass flux, inlet pressure, inlet quality, tube diameter, etc. using the two sets of flow boiling correlations are compared against the experimental data. Overall, the Thermal Desktop model with the new universal cryogenic flow boiling correlations demonstrates an improvement in predictive performance over the Thermal Desktop model using built-in correlations for both wall temperature and location of critical heat flux compared to the data for LH2 flow boiling in heated tubes.

Cryogenic Propellant Transfer↗

Validation of Universal Cryogenic Flow Boiling Correlations in Thermal Desktop for Liquid Hydrogen

Developing accurate models of two-phase cryogenic flow will reduce risk and reduce margins for future NASA vehicles such as the Nuclear Thermal Propulsion (NTP) system. Currently there is a need for more accurate, direct cryogenic data-anchored models for various boiling and two-phase phenomena. The focus of the current work is on modeling steady state cryogenic flow boiling in the transfer line that connects a propellant storage tank to an engine or customer receiver tank. This paper presents Thermal Desktop model validation results against several historical liquid hydrogen (LH2) heated tube experiments that cover a wide range of inlet conditions, mass flux, and heat flux. New universal cryogenic flow boiling correlations were recently developed and anchored to the largest cryogenic flow boiling database assembled in the world to-date. The new universal correlations are patched together and implemented into Thermal Desktop to compare model performance against the built-in flow boiling correlations. Axial wall temperature predictions as a function of preponderant parameters like heat flux, mass flux, inlet pressure, inlet quality, tube diameter, etc. using the two sets of flow boiling correlations are compared against the experimental data. Overall, the Thermal Desktop model with the new universal cryogenic flow boiling correlations demonstrates an improvement in predictive performance over the Thermal Desktop model using built-in correlations for both wall temperature and location of critical heat flux compared to the data for LH2 flow boiling in heated tubes.

cryogenic propellant transfer↗

Battery Performance and Cost Model (BatPaC) Version 6.0

SF-26-016 The Battery Performance and Cost model (BatPaC) is a calculation method based on Microsoft Excel spreadsheets that has been developed at Argonne for estimating the performance and manufacturing cost of lithium-ion batteries for electric-drive vehicles including hybrid-electrics (HEV), plug-in hybrids (PHEVs) and pure electrics. BatPaC was first developed in 2007, was subsequently peer reviewed, and it has served Argonne researchers and the greater battery community in studying the impact of material properties on performance at the pack level. BatPaC has been updated and re-released multiple times since its original public release in 2011. This current version is BatPaC 6.0, which contains additional functionality needed to handle advances in automotive batteries, like the use of lithium metal and silicon anodes and the need to accommodate cell expansion and apply high levels of pressure.

KNEHR, KEVIN [Argonne National Laboratory (ANL), A↗

Performance Evaluation of the NASA/KSC Transmission System

NASA-KSC currently uses three bridged 100-Mbps FDDI segments as its backbone for data traffic. The FDDI Transmission System (FTXS) connects the KSC industrial area, KSC launch complex 39 area, and the Cape Canaveral Air Force Station. The report presents a performance modeling study of the FTXS and the proposed ATM Transmission System (ATXS). The focus of the study is on performance of MPEG video transmission on these networks. Commercial modeling tools - the CACI Predictor and Comnet tools - were used. In addition, custom software tools were developed to characterize conversation pairs in Sniffer trace (capture) files to use as input to these tools. A baseline study of both non-launch and launch day data traffic on the FTXS is presented. MPEG-1 and MPEG-2 video traffic was characterized and the shaping of it evaluated. It is shown that the characteristics of a video stream has a direct effect on its performance in a network. It is also shown that shaping of video streams is necessary to prevent overflow losses and resulting poor video quality. The developed models can be used to predict when the existing FTXS will 'run out of room' and for optimizing the parameters of ATM links used for transmission of MPEG video. Future work with these models can provide useful input and validation to set-top box projects within the Advanced Networks Development group in NASA-KSC Development Engineering.

Christensen, Kenneth J.↗

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↗

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↗

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↗

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↗

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.↗

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↗

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↗

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↗

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.↗

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↗