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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ASSESSING FUEL PROPERTY EFFECTS ON CAVITATION AND EROSION PROPENSITY USING A COMPUTATIONAL FUEL SCREENING TOOL

To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.

cavitation-induced erosion↗
DOE OSTI2023

Local Bayesian Dirichlet mixing of imperfect models

Abstract To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several imperfect models. This framework can be viewed as an extension of Bayesian stacking. To illustrate the method, we study the ability of Bayesian model averaging and mixing techniques to mine nuclear masses. We show that the global and local mixtures of models reach excellent performance on both prediction accuracy and uncertainty quantification and are preferable to classical Bayesian model averaging. Additionally, our statistical analysis indicates that improving model predictions through mixing rather than mixing of corrected models leads to more robust extrapolations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Large format imaging arrays for the Atacama Cosmology Telescope

We describe progress in the fabrication, characterization, and production of detector arrays for the Atacama Cosmology Telescope (ACT). The completed ACT instrument is specified to image simultaneously at 145, 225, and 265 GHz using three 32x32 filled arrays of superconducting transition edge sensors (TES) read out with time-division-multiplexed SQUID amplifiers. We present details of the pixel design and testing including the optimization of the electrical parameters for multiplexed readout. Using geometric noise suppression and careful tuning of operation temperature and device bias resistance, the excess noise in the TES devices is balanced with detector speed for interfacing with the ACT optics. The design also accounts for practical tolerances such as transition temperature gradients and scatter that occur in the production of multiple wafers to populate fully the kilopixel cameras. We have developed an implanted absorber layer compatible with our silicon-on-insulator process that allows for tunable optical resistance with requisite on-wafer uniformity and wafer-to-wafer reproducibility. Arrays of 32 elements have been tested in the laboratory environment including electrical, optical, and multiplexed performance. Given this pixel design, optical tests and modeling are used to predict the performance of the filled array under anticipated viewing conditions. Integration of the filled array of pixels with a tuned backshort and dielectric plate in front of the array maximize absorption and the focal plane and suppress reflections. A mechanical design for the build of the full structure is completed and we report on progress toward the construction of a prototype array for first light on the ACT.

Chervenak, J. A.↗
DOE OSTI2024

Improving Low‐Cloud Fraction Prediction Through Machine Learning

Abstract In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of “too few” low clouds and “too rapid” SCT in global climate models.

Geology↗
NASA NTRS2020

A Statistical Model to Predict the Extratropical Transition of Tropical Cyclones

This paper introduces a logistic regression model for the extratropical transition(ET) of tropical cyclones in the North Atlantic and the Western North Pacific, using elastic net regularization to select predictors and estimate coefficients.Predictors are chosen from the 1979-2017 best track and reanalysis datasets, and verification is done against the tropical/extratropical labels in the best track data. In an independent test set, the model skillfully predicts ET at lead times up to two days, with latitude and sea surface temperature as its most important predictors. At a lead time of 24 h, it predicts ET with a Matthews correlation coefficient of 0.4 in the North Atlantic, and 0.6 in the Western North Pacific. It identifies 80% of storms undergoing ET in the North Atlantic, and 92% of those in the Western North Pacific. 90% of transition time errors are less than 24 h. Select examples of the model's performance on individual storms illustrate its strengths and weaknesses.Two versions of the model are presented: an "operational model" that may provide baseline guidance for operational forecasts, and a "hazard model"that can be integrated into statistical TC risk models. As instantaneous diagnostics for tropical/extratropical status, both models' zero lead time predictions perform about as well as the widely used Cyclone Phase Space (CPS) in the Western North Pacific and better than the CPS in the North Atlantic, and predict the timings of the transitions better than CPS in both basins.

Melanie Bieli↗
NASA NTRS2017

CEDAR-GEM Challenge for Systematic Assessment of Ionosphere/Thermosphere Models in Predicting TEC During the 2006 December Storm Event

In order to assess current modeling capability of reproducing storm impacts on total electron content (TEC), we considered quantities such as TEC, TEC changes compared to quiet time values, and the maximum value of the TEC and TEC changes during a storm. We compared the quantities obtained from ionospheric models against ground-based GPS TEC measurements during the 2006 AGU storm event (14-15 December 2006) in the selected eight longitude sectors. We used 15 simulations obtained from eight ionospheric models, including empirical, physics-based, coupled ionosphere-thermosphere, and data assimilation models. To quantitatively evaluate performance of the models in TEC prediction during the storm, we calculated skill scores such as RMS error, Normalized RMS error (NRMSE), ratio of the modeled to observed maximum increase (Yield), and the difference between the modeled peak time and observed peak time. Furthermore, to investigate latitudinal dependence of the performance of the models, the skill scores were calculated for five latitude regions. Our study shows that RMSE of TEC and TEC changes of the model simulations range from about 3 TECU (total electron content unit, 1 TECU = 1016 el m2) (in high latitudes) to about 13 TECU (in low latitudes), which is larger than latitudinal average GPS TEC error of about 2 TECU. Most model simulations predict TEC better than TEC changes in terms of NRMSE and the difference in peak time, while the opposite holds true in terms of Yield. Model performance strongly depends on the quantities considered, the type of metrics used, and the latitude considered.

Shim, J. S.↗
NASA NTRS1979

Evaluation of models to predict insolation on tilted surfaces

An empirical study was performed to evaluate the validity of various insolation models which employ either an isotropic or an anisotropic distribution approximation for sky light when predicting insolation on tilted surfaces. Data sets of measured hourly insolation values were obtained over a 6-month period using pyranometers which received diffuse and total solar radiation on a horizontal plane and total radiation on surfaces tilted toward the equator at 37 degrees and 60 degrees angles above the horizon. Data on the horizontal surfaces were used in the insolation models to predict insolation on the tilted surface; comparisons of measured vs calculated insolation on the tilted surface were examined to test the validity of the sky light approximations. It was found that the Liu-Jordan isotropic distribution model provides a good fit to empirical data under overcast skies but underestimates the amount of solar radiation incident on tilted surfaces under clear and partly cloudy conditions.

Klucher, T. M.↗
NASA NTRS2014

Predicting Ground Delay Program at an Airport Based on Meteorological Conditions

In this paper, we present two supervised-learning models, logistic regression and decision tree, to predict occurrence of ground delay program at an airport based on meteorological conditions and scheduled traffic demand. Such predictive capabilities can help the Federal Aviation Administration traffic managers and airline dispatchers to prepare mitigation strategies to reduce the impact of adverse weather. The models are applied to predict ground delay program occurrence at two major U.S. airports: Newark Liberty Intl. and San Francisco Intl. airports. The logistic regression model estimates the probability that a ground delay program will occur during a given hour. Decision tree, on the other hand, classifies an hour as a ground delay program or not based on the input variables. Results indicate that both models perform significantly better than a purely random prediction of ground delay program occurrence at the two airports. The logistic regression model performs better than the decision tree model. The degree to which various input variables impact the probability of ground delay program vary between the two airports. While the enroute convective weather is a dominant factor causing ground delay programs at New York airports, poor visibility and low cloud ceiling caused by marine stratus are major drivers of ground delay programs at San Francisco Intl. airport.

traffic flow management↗
NASA NTRS1991

Models of remote manipulation in space

Robots involved in high value manipulation must be effectively coupled to a human operator either at the work-site or remotely connected via communication links. In order to make use of experimental performance evaluation data, models must be developed. Powerful models of remote manipulation by humans can be used to predict manipulation performance in future systems based on today's laboratory systems. In this paradigm, the models are developed from experimental data, and then used to predict performance in slightly different situations. Second, accurate telemanipulation will allow design of manipulation systems which extend manipulation capability beyond its current bounds.

Hannaford, Blake↗
DOE OSTI2021

Multi-fidelity electrochemical modeling of thermally activated battery cells

Thermally activated batteries undergo a series of coupled physical changes during activation that influence battery performance. These processes include energetic material burning, heat transfer, electrolyte phase change, capillary-driven two-phase porous flow, ion transport, electrochemical reactions, and electrical transport. Several of these processes are strongly coupled and have a significant effect on battery performance, but others have minimal impact or may be suitably represented by reduced-order models. Additionally, assessing the relative importance of these phenomena must be based on comparisons to a high-fidelity model including all known processes. In this work, we first present and demonstrate a high-fidelity, multi-physics model of electrochemical performance. This novel multi-physics model enables predictions of how competing physical processes affect battery performance and provides unique insights into the difficult-to-measure processes that happen during battery activation. We introduce four categories of model fidelity that include different physical simplifications, assumptions, and reduced-order models to decouple or remove costly elements of the simulation. Using this approach, we show an order-of-magnitude reduction in computational cost while preserving all design-relevant quantities of interest within 5 percent. The validity of this approach and these model reductions is demonstrated by comparison between results from the full fidelity model and the different reduced models.

25 ENERGY STORAGE↗
NASA NTRS2002

Investigation of Fundamental Modeling and Thermal Performance Issues for a Metallic Thermal Protection System Design

A study was performed to develop an understanding of the key factors that govern the performance of metallic thermal protection systems for reusable launch vehicles. A current advanced metallic thermal protection system (TPS) concept was systematically analyzed to discover the most important factors governing the thermal performance of metallic TPS. A large number of relevant factors that influence the thermal analysis and thermal performance of metallic TPS were identified and quantified. Detailed finite element models were developed for predicting the thermal performance of design variations of the advanced metallic TPS concept mounted on a simple, unstiffened structure. The computational models were also used, in an automated iterative procedure, for sizing the metallic TPS to maintain the structure below a specified temperature limit. A statistical sensitivity analysis method, based on orthogonal matrix techniques used in robust design, was used to quantify and rank the relative importance of the various modeling and design factors considered in this study. Results of the study indicate that radiation, even in small gaps between panels, can reduce significantly the thermal performance of metallic TPS, so that gaps should be eliminated by design if possible. Thermal performance was also shown to be sensitive to several analytical assumptions that should be chosen carefully. One of the factors that was found to have the greatest effect on thermal performance is the heat capacity of the underlying structure. Therefore the structure and TPS should be designed concurrently.

Blosser, Max L.↗
NASA NTRS1998

Computational Model of Human and System Dynamics in Free Flight: Studies in Distributed Control Technologies

This paper presents a set of studies in full mission simulation and the development of a predictive computational model of human performance in control of complex airspace operations. NASA and the FAA have initiated programs of research and development to provide flight crew, airline operations and air traffic managers with automation aids to increase capacity in en route and terminal area to support the goals of safe, flexible, predictable and efficient operations. In support of these developments, we present a computational model to aid design that includes representation of multiple cognitive agents (both human operators and intelligent aiding systems). The demands of air traffic management require representation of many intelligent agents sharing world-models, coordinating action/intention, and scheduling goals and actions in a potentially unpredictable world of operations. The operator-model structure includes attention functions, action priority, and situation assessment. The cognitive model has been expanded to include working memory operations including retrieval from long-term store, and interference. The operator's activity structures have been developed to provide for anticipation (knowledge of the intention and action of remote operators), and to respond to failures of the system and other operators in the system in situation-specific paradigms. System stability and operator actions can be predicted by using the model. The model's predictive accuracy was verified using the full-mission simulation data of commercial flight deck operations with advanced air traffic management techniques.

Corker, Kevin M.↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
NASA NTRS1984

Hover test of a full-scale hingeless rotor

The performance and aeroelastic stability in hover of a 9.8-m diameter, hingeless helicopter rotor system was evaluated. Rotor performance and inplane damping data were obtained for rotor operation between 350 and 425 rpm for thrust coefficients (CT/sigma) between 0.0 and 0.12. At constant rotor thrust, a minimum in rotor inplane damping was measured at 400 rpm. Good agreement is shown between experimental performance data and predicted performance. The influence of different aerodynamic inflow models on predicting damping levels is also shown. The best correlation with experimental stability data was obtained when a dynamic inflow model was used instead of static or quasistatic inflow models. Comparison with other full scale, hingeless rotor data in hover is presented. The hingeless rotor data and data from a full scale, bearingless main rotor test performed on the same general purpose test apparatus were compared. Although the bearingless rotor was more highly damped at design tip speed and 1-g thrust operation, greater sensitivity to operating conditions is shown. At low thrust levels the bearingless main rotor is less damped than the hingeless rotor.

Warmbrodt, W.↗
NASA NTRS1984

Hover test of a full-scale hingeless rotor

The performance and aeroelastic stability in hover of a 9.8-m diameter, hingeless helicopter rotor system was evaluated. Rotor performance and inplane damping data were obtained for rotor operation beteen 350 and 425 rpm for thrust coefficients (CT/sigma) between 0.0 and 0.12. At constant rotor thrust, a minimum in rotor inplane damping was measured at 400 rpm. Good agreement is shown between experimental performance data and predicted performance. The influence of different aerodynamic inflow models on predicting damping levels is also shown. The best correlation with experimental stability data was obtained when a dynamic inflow model was used instead of static or quasistatic inflow models. Comparison with other full scale, hingeless rotor data in hover is presented. The hingeless rotor data and data from a full scale, bearingless main rotor test performed on the same general purpose test apparatus were compared. Although the bearingless rotor was more highly damped at design tip speed and 1-g thrust operation, greater sensitivity to operating conditions is shown. At low thrust levels the bearingless main rotor is less damped than the hingeless rotor.

Warmbrodt, W.↗
DOE OSTI2024

Core performance predictions in projected SPARC first-campaign plasmas with nonlinear CGYRO

This work characterizes the core transport physics of SPARC early-campaign plasmas using the PORTALS-CGYRO framework. Empirical modeling of SPARC plasmas with L-mode confinement indicates an ample window of breakeven (Q > 1) without the need of H-mode operation. Extensive modeling of multi-channel (electron energy, ion energy, and electron particle) flux-matched conditions with the nonlinear CGYRO code for turbulent transport coupled to the macroscopic plasma evolution using PORTALS reveals that the maximum fusion performance to be attained will be highly dependent on the near-edge pressure. Stiff core transport conditions are found, particularly when fusion gain approaches unity, and predicted density peaking is found to be in line with empirical databases of particle source-free H-modes. Impurity optimization is identified as a potential avenue to increase fusion performance while enabling core-edge integration. Extensive validation of the quasilinear TGLF model builds confidence in reduced-model predictions. The implications of projecting L-mode performance to high-performance and burning-plasma devices is discussed, together with the importance of predicting edge conditions.

Rodriguez-Fernandez, P. (ORCID:0000000273611131)↗
NASA NTRS1996

Tilt Rotor Aircraft Aeroacoustics

A fleet of civil tilt rotor transports offers a means of reducing airport congestion and point-to-point travel time. The speed, range, and fuel economy of these aircraft, along with their efficient use of vertiport area, make them good candidates for short-to-medium range civil transport. However, to be successfully integrated into the civilian community, the tilt rotor must be perceived as a quiet, safe, and economical mode of transportation that does not harm the environment. In particular, noise impact has been identified as a possible barrier to the civil tilt rotor. Along with rotor conversion-mode flight, and blade-vortex interaction noise during descent, hover mode is a noise problem for tilt rotor operations. In the present research, tilt rotor hover aeroacoustics have been studied analytically, experimentally, and computationally. Various papers on the subject were published as noted in the list of publications. More recently, experimental measurements were made on a 1/12.5 scale model of the XV-15 in hover and analyses of this data and extrapolations to full scale were also carried out. A dimensional analysis showed that the model was a good aeroacoustic approximation to the full-scale aircraft, and scale factors were derived to extrapolate the model measurements to the full-scale XV-15. The experimental measurements included helium bubble flow visualization, silk tuft flow visualization, 2-component hot wire anemometry, 7-hole pressure probe measurements, vorticity measurements, and outdoor far field acoustic measurements. The hot wire measurements were used to estimate the turbulence statistics of the flow field into the rotors, such as length scales, velocity scales, dissipation, and turbulence intermittency. Several different configurations of the model were tested: (1) standard configurations (single isolated rotor, two rotors without the aircraft, standard tilt rotor configuration); (2) flow control devices (the 'plate', the 'diagonal fences'); (3) basic configuration changes (increasing the rotor/rotor spacing, reducing the rotor plane/wing clearance. operating the rotors out of phase). Also, an approximation to Sikorsky's Variable Diameter Tilt Rotor (VDTR) configuration was tested, and some flow measurements were made on a semi-span configuration of the model. Acoustic predictions were made using LOWSON.M, a Mathematica code Mean aerodynamic models were developed based on hover performance predictions from HOVER.FOR. This hover prediction code used blade element theory for the aerodynamics, and Prandtl's Vortex theory to model the wake, along with empirical formulas for the effects of Reynolds number, Mach number, and stall. Aerodynamic models were developed from 7-hole pressure probe measurements of the mean velocity into the model rotors.

George, Albert R.↗
NASA NTRS1979

An averaging battery model for a lead-acid battery operating in an electric car

A battery model is developed based on time averaging the current or power, and is shown to be an effective means of predicting the performance of a lead acid battery. The effectiveness of this battery model was tested on battery discharge profiles expected during the operation of an electric vehicle following the various SAE J227a driving schedules. The averaging model predicts the performance of a battery that is periodically charged (regenerated) if the regeneration energy is assumed to be converted to retrievable electrochemical energy on a one-to-one basis.

Bozek, J. M.↗