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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,099 records · Page 61

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NASA NTRS1985

An error-dependent model of instrument-scanning behavior in commercial airline pilots

A new flexible model of pilot instrument scanning behavior is presented which assumes that the pilot uses a set of deterministic scanning patterns on the pilot's perception of error in the state of the aircraft, and the pilot's knowledge of the interactive nature of the aircraft's systems. Statistical analyses revealed that a three stage Markov process composed of the pilot's three predicted lookpoints (LP), occurring 1/30, 2/30, and 3/30 of a second prior to each LP, accurately modelled the scanning behavior of 14 commercial airline pilots while flying steep turn maneuvers in a Boeing 737 flight simulator. The modelled scanning data for each pilot were not statistically different from the observed scanning data in comparisons of mean dwell time, entropy, and entropy rate. These findings represent the first direct evidence that pilots are using deterministic scanning patterns during instrument flight. The results are interpreted as direct support for the error dependent model and suggestions are made for further research that could allow for identification of the specific scanning patterns suggested by the model.

Jones, D. H.↗

NASA NTRS1991

Identification and control of NASA's ACES structures

Results are presented of identification and control experiments on NASA's ACES structure at the Marshall Space Flight Center. The models used for controller design were obtained from identification experiments employing the algorithm Q-Markov cover. The OVC algorithm used for control design produces a controller minimizing the control energy of the closed-loop system, subject to inequality constraints on each of the output variances. The identified model matches the experimental data for the ACES structure reasonably well. The line of sight pointing errors of the structure are substantially reduced by the controllers.

Liu, K.↗

NASA NTRS2007

F-15B Quiet Spike(TradeMark) Aeroservoelastic Flight-Test Data Analysis

System identification is utilized in the aerospace community for development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlin ear systems. In this report, we show the application of one such nonlinear system identification technique, structure detection, for the an alysis of Quiet Spike(TradeMark)(Gulfstream Aerospace Corporation, Savannah, Georgia) aeroservoelastic flight-test data. Structure detectio n is concerned with the selection of a subset of candidate terms that best describe the observed output. Structure computation as a tool fo r black-box modeling may be of critical importance for the development of robust, parsimonious models for the flight-test community. The ob jectives of this study are to demonstrate via analysis of Quiet Spike(TradeMark) aeroservoelastic flight-test data for several flight conditions that: linear models are inefficient for modelling aeroservoelast ic data, nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data an d the model structure and parameters vary as the flight condition is altered.

Kukreja, Sunil L.↗

NASA NTRS1987

Identification of structural interface characteristics using component mode synthesis

The inability to adequately model connections has limited the ability to predict overall system dynamic response. Connections between structural components are often mechanically complex and difficult to accurately model analytically. Improved analytical models for connections are needed to improve system dynamic predictions. This study explores combining Component Mode synthesis methods for coupling structural components with Parameter Identification procedures for improving the analytical modeling of the connections. Improvements in the connection properties are computed in terms of physical parameters so the physical characteristics of the connections can be better understood, in addition to providing improved input for the system model. Two sample problems, one utilizing simulated data, the other using experimental data from a rotor dynamic test rig are presented.

Huckelbridge, A. A.↗

NASA NTRS1987

Identification of structural interface characteristics using component mode synthesis

Huckelbridge, A. A.↗

NASA NTRS1989

Identification of structural interface characteristics using component mode synthesis

Huckelbridge, A. A.↗

DOE OSTI2018

Parameter covariance and non-uniqueness in material model calibration using the Virtual Fields Method

Traditionally, material identification is performed using global load and displacement data from simple boundary-value problems such as uni-axial tensile and simple shear tests. More recently, however, inverse techniques such as the Virtual Fields Method (VFM) that capitalize on heterogeneous, full-field deformation data have gained popularity. In this work, we have written a VFM code in a finite-deformation framework for calibration of a viscoplastic (i.e. strain-rate dependent) material model for 304L stainless steel. Using simulated experimental data generated via finite-element analysis (FEA), we verified our VFM code and compared the identified parameters with the reference parameters input into the FEA. The identified material model parameters had surprisingly large error compared to the reference parameters, which was traced to parameter covariance and the existence of many essentially equivalent parameter sets. This parameter non-uniqueness and its implications for FEA predictions is discussed in detail. Lastly, we present two strategies to reduce parameter covariance – reduced parametrization of the material model and increased richness of the calibration data – which allow for the recovery of a unique solution.

304L stainless steel↗

NASA NTRS1991

Attitude identification for SCOLE using two infrared cameras

An algorithm is presented that incorporates real time data from two infrared cameras and computes the attitude parameters of the Spacecraft COntrol Lab Experiment (SCOLE), a lab apparatus representing an offset feed antenna attached to the Space Shuttle by a flexible mast. The algorithm uses camera position data of three miniature light emitting diodes (LEDs), mounted on the SCOLE platform, permitting arbitrary camera placement and an on-line attitude extraction. The continuous nature of the algorithm allows identification of the placement of the two cameras with respect to some initial position of the three reference LEDs, followed by on-line six degrees of freedom attitude tracking, regardless of the attitude time history. A description is provided of the algorithm in the camera identification mode as well as the mode of target tracking. Experimental data from a reduced size SCOLE-like lab model, reflecting the performance of the camera identification and the tracking processes, are presented. Computer code for camera placement identification and SCOLE attitude tracking is listed.

Shenhar, Joram↗

NASA NTRSDate not supplied

Development of the Mod II X-57 Piloted Simulator and Flying Qualities Predictions

This paper discusses the development of the X-57 Mod II piloted simulator along with the predicted flight dynamics of the airplane. The piloted simulator models were initially based on data published by Tecnam on a P2006T airplane but were further improved upon through parameter identification of flight data as well as modeling tools such as computational fluid dynamics. In addition to having accurate flight models, a realistic cockpit was constructed to aid pilot training. From the piloted simulator, an understanding of how the airplane will behave throughout the flight envelope using established FAR and MIL standards is discussed. Using the simulation results, this paper will show that the airplane is predicted to be statically and dynamically stable as well as having Level 1 flying qualities.

Ryan Wallace↗

DOE OSTI2017

Identification of a new class of potent Cdc7 inhibitors designed by putative pharmacophore model: Synthesis and biological evaluation of 2,3-dihydrothieno[3,2-d]pyrimidin-4(1H)-ones

Not Available

Kurasawa, Osamu↗

DOE OSTI2023

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. In this study, we developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Identification of key parameters controlling demographically structured vegetation dynamics in a Land Surface Model

This dataset includes outputs of 5000 simulations of CLM4.5(ED) to quantify the sensitivity of the model outputs to changes in model parameters using the Fourier Amplitude Sensitivity Test (FAST). Each simulation is generated by simultaneously sampling from 15% deviations of the default values of >80 vegetation parameters. This dataset includes 1) model codes of CLM4.5(ED) used for the sensitivity analysis; 2) the parameter samples; and 3) the corresponding model outputs of vegetation status (e.g., Gross Primary Production, Leaf Area Index and Biomass) and demography (e.g., diameter growth and mortality rates). The outputs are organized to the format of the FAST toolbox (https://sites.google.com/site/xuchongang/uasatoolbox).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Characterization of the Dynamics of Climate Systems and Identification of Missing Mechanisms Impacting the Long Term Predictive Capabilities of Global Climate Models Utilizing Dynamical Systems Approaches to the Analysis of Observed and Modeled Climate

The goal of this research was to apply fractional and non-linear analysis techniques in order to develop a more complete characterization of climate change and variability for the oceanic, sea ice and atmospheric components of the Earth System. This research applied two measures of dynamical characteristics of time series, the R/S method of calculating the Hurst exponent and Renyi entropy, to observational and modeled climate data in order to evaluate how well climate models capture the long-term dynamics evident in observations. Fractional diffusion analysis was applied to ARGO ocean buoy data to quantify ocean transport. Self organized maps were applied to North Pacific sea level pressure and analyzed in ways to improve seasonal predictability for Alaska fire weather. This body of research shows that these methods can be used to evaluate climate models and shed light on climate mechanisms (i.e., understanding why something happens). With further research, these methods show promise for improving seasonal to longer time scale forecasts of climate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Uncertainty, Sensitivity Analysis, and Causal Identification in the Arctic using a Perturbed Parameter Ensemble of the HiLAT Climate Model

Coupled climate models have a large number of input parameters that can affect output uncertainty. We conducted a sensitivity analysis of sea ice proper:es and Arc:c related climate variables to 5 parameters in the HiLAT climate model: air-ocean turbulent exchange parameter (C), conversion of water vapor to clouds (cldfrc_rhminl) and of ice crystals to snow (micro_mg_dcs), snow thermal conduc:vity (ksno), and maximum snow grain size (rsnw_mlt). We used an elementary effect (EE) approach to rank their importance for output uncertainty. EE is an extension of one-at-a-time sensitivity analyses, but it is more efficient in sampling multi-dimensional parameter spaces. We looked for emerging relationships among climate variables across the model ensemble, and used causal discovery algorithms to establish potential pathways for those relationships.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES)

Vegetation plays an important role in regulating global carbon cycles and is a key component of the Earth system models (ESMs) that aim to project Earth's future climate. In the last decade, the vegetation component within ESMs has witnessed great progress from simple “big-leaf” approaches to demographically structured approaches, which have a better representation of plant size, canopy structure, and disturbances. These demographically structured vegetation models typically have a large number of input parameters, and sensitivity analysis is needed to quantify the impact of each parameter on the model outputs for a better understanding of model behavior. In this study, we conducted a comprehensive sensitivity analysis to diagnose the Community Land Model coupled to the Functionally Assembled Terrestrial Simulator, or CLM4.5(FATES). Specifically, we quantified the first- and second-order sensitivities of the model parameters to outputs that represent simulated growth and mortality as well as carbon fluxes and stocks for a tropical site with an extent of 1 x 1°. While the photosynthetic capacity parameter (V c,max25 )is found to be important for simulated carbon stocks and fluxes, we also show the importance of carbon storage and allometry parameters, which determine survival and growth strategies within the model. The parameter sensitivity changes with different sizes of trees and climate conditions. The results of this study highlight the importance of understanding the dynamics of the next generation of demographically enabled vegetation models within ESMs to improve model parameterization and structure for better model fidelity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

QSAR model for human pregnane X receptor (PXR) binding: Screening of environmental chemicals and correlations with genotoxicity, endocrine disruption and teratogenicity

The pregnane X receptor (PXR) has a key role in regulating the metabolism and transport of structurally diverse endogenous and exogenous compounds. Activation of PXR has the potential to initiate adverse effects, causing drug–drug interactions, and perturbing normal physiological functions. Therefore, identification of PXR ligands would be valuable information for pharmaceutical and toxicological research. In the present study, we developed a quantitative structure–activity relationship (QSAR) model for the identification of PXR ligands using data based on a human PXR binding assay. A total of 631 molecules, representing a variety of chemical structures, constituted the training set of the model. Cross-validation of the model showed a sensitivity of 82%, a specificity of 85%, and a concordance of 84%. The developed model provided knowledge about molecular descriptors that may influence the binding of molecules to PXR. The model was used to screen a large inventory of environmental chemicals, of which 47% was found to be within domain of the model. Approximately 35% of the chemicals within domain were predicted to be PXR ligands. The predicted PXR ligands were found to be overrepresented among chemicals predicted to cause adverse effects, such as genotoxicity, teratogenicity, estrogen receptor activation and androgen receptor antagonism compared to chemicals not causing these effects. The developed model may be useful as a tool for predicting potential PXR ligands and for providing mechanistic information of toxic effects of chemicals. -- Highlights: ► Global QSAR model for the identification of PXR ligands was developed. ► Molecular descriptors that may influence PXR binding were identified. ► 35% of a large set of environmental chemicals were predicted to be PXR ligands. ► Predicted PXR binding was associated with various adverse effects.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1974

A new criterion for modeling systems

The criterion that is proposed is an expected value of the mean square response error as an alternative to testing a model against new data. Modeling with respect to this new criterion does not change the estimate for a given model format from a maximum likelihood estimate or mean square response error estimate. The new criterion does, however, provide a means of comparing models with different formats and varying complexity. A numerical example is used to illustrate the application of the proposed criteria and the problem of searching for the best model. For all but the most trivial system identification problems, it is shown that a prohibitive number of combinations of terms of the model must be investigated to ensure the final model is best.

Taylor, L. W., Jr.↗

DOE OSTI2011

Precision of electromagnetic control of a quantum system

Coherent control of a quantum system is limited both by the decoherence due to environment and the quantum nature of the control agent. The high fidelity of control demanded by fault-tolerant quantum computation and the intrinsic interest in nonclassical effects from the interplay between control and dissipation are motivations for a detailed study of the interaction dynamics between the quantum system and the macroscopic environment and control agent. We present a detailed time-evolution study of a two-level system interacting with a laser pulse and the electromagnetic vacuum in the multimode Jaynes-Cummings model. A diagrammatic formalism allows easy identification of coherent dynamics and relaxation of the two-level system. We demonstrate a computational method of dynamics with precise error bounds for fast operations versus slow decoherence, spanning the Markovian and non-Markovian regimes. Comparison against an exact model solution of our results with existing approximations of the master equation shows the lack of accuracy in the latter.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗