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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced Performance Metrics and their Application to the Sensitivity Analysis for Model Validation and Calibration

High-quality generator dynamic models are critical to reliable and accurate power systems studies and planning. With the availability of PMUs, measurement-based approach for model validation has gained significant prominence. In this approach, the quality of a model is analyzed by visually comparing measured generator response with the model-based simulated response for large system disturbances. This paper proposes a new set of performance metrics to assess the model validation results to facilitate automation of the model validation process. In the proposed methodology, first, the slow governor response and comparatively faster oscillatory response are separated, and then a separate set of performance metrics is calculated for each of these two components. These proposed metrics quantify the mismatch between the actual and model-based response in a comprehensive manner without missing any information enabling automation of the process. Furthermore, in this paper, we are also proposing that the sensitivity analysis for model calibration be performed with respect to the proposed metrics for the systematic identification of key parameters. In this work, results obtained using both simulated and real-world case-studies validate the effectiveness of the proposed performance metrics for model validation and their application to the sensitivity analysis for model calibration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1999

Fidelity Analysis of Sampled Imaging Systems

Many modeling, simulation and performance analysis studies of sampled imaging systems are inherently incomplete because they are conditioned on a discrete-input, discrete-output model that only accounts for blurring during image acquisition and additive noise. For those sampled imaging systems where the effects of digital image acquisition, digital filtering and reconstruction are significant, the modeling, simulation and performance analysis should be based on a more comprehensive continuous-input, discrete-processing, continuous-output end-to-end model. This more comprehensive model should properly account for the low-pass filtering effects of image acquisition prior to sampling, the potentially important noiselike effects of the aliasing caused by sampling, additive noise due to device electronics and quantization, the generally high-boost filtering effects of digital processing, and the low-pass filtering effects of image reconstruction. This model should not, however, be so complex as to preclude significant mathematical analysis, particularly the mean-square (fidelity) type of analysis so common in linear system theory. We demonstrate that, although the mathematics of such a model is more complex, the increase in complexity is not so great as to prevent a complete fidelity-metric analysis at both the component level and at the end-to-end system level: that is, computable mean-square-based fidelity metrics are developed by which both component-level and system-level performance can be quantified. In addition, we demonstrate that system performance can be assessed qualitatively by visualizing the output image as the sum of three component images, each of which relates to a corresponding fidelity metric. The cascaded, or filtered, component accounts for the end-to-end system filtering of image acquisition, digital processing, and image reconstruction; the random noise component accounts for additive random noise, modulated by digital processing and image reconstruction filtering; and the aliased noise component accounts for the frequency folding effect of sampling, modulated by digital processing and image reconstruction filtering.

Park, Stephen K.↗
NASA NTRS1994

A performance analysis method for distributed real-time robotic systems: A case study of remote teleoperation

Robot coordination and control systems for remote teleoperation applications are by necessity implemented on distributed computers. Modeling and performance analysis of these distributed robotic systems is difficult, but important for economic system design. Performance analysis methods originally developed for conventional distributed computer systems are often unsatisfactory for evaluating real-time systems. The paper introduces a formal model of distributed robotic control systems; and a performance analysis method, based on scheduling theory, which can handle concurrent hard-real-time response specifications. Use of the method is illustrated by a case of remote teleoperation which assesses the effect of communication delays and the allocation of robot control functions on control system hardware requirements.

Lefebvre, D. R.↗
NASA NTRS2008

SEE Rate Estimation: Model Complexity and Data Requirements

Statistical Methods outlined in [Ladbury, TNS20071 can be generalized for Monte Carlo Rate Calculation Methods Two Monte Carlo Approaches: a) Rate based on vendor-supplied (or reverse-engineered) model SEE testing and statistical analysis performed to validate model; b) Rate calculated based on model fit to SEE data Statistical analysis very similar to case for CREME96. Information Theory allows simultaneous consideration of multiple models with different complexities: a) Model with lowest AIC usually has greatest predictive power; b) Model averaging using AIC weights may give better performance if several models have similar good performance; and c) Rates can be bounded for a given confidence level over multiple models, as well as over the parameter space of a model.

Ladbury, Ray↗
NASA NTRS1981

Some aspects of algorithm performance and modeling in transient analysis of structures

The status of an effort to increase the efficiency of calculating transient temperature fields in complex aerospace vehicle structures is described. The advantages and disadvantages of explicit algorithms with variable time steps, known as the GEAR package, is described. Four test problems, used for evaluating and comparing various algorithms, were selected and finite-element models of the configurations are described. These problems include a space shuttle frame component, an insulated cylinder, a metallic panel for a thermal protection system, and a model of the wing of the space shuttle orbiter. Results generally indicate a preference for implicit over explicit algorithms for solution of transient structural heat transfer problems when the governing equations are stiff (typical of many practical problems such as insulated metal structures).

Adelman, H. M.↗
NASA NTRS1999

A Preliminary Model for Spacecraft Propulsion Performance Analysis Based on Nuclear Gain and Subsystem Mass-Power Balances

Rapid transportation of human crews to destinations throughout the solar system will require propulsion systems having not only very high exhaust velocities (i.e., I(sub sp) >= 10(exp 4) to 10(exp 5) sec) but also extremely low mass-power ratios (i.e., alpha <= 10(exp -2) kg/kW). These criteria are difficult to meet with electric propulsion and other power-limited systems, but may be achievable with propulsion concepts that use onboard power to produce a net gain in energy via fusion or some other nuclear process. This paper compares the fundamental performance of these gain-limited systems with that of power-limited systems, and determines from a generic power balance the gains required for ambitious planetary missions ranging up to 100 AU. Results show that energy gain reduces the required effective mass-power ratio of the system, thus enabling shorter trip times than those of power-limited concepts.

Chakrabarti, S.↗
NASA NTRS2009

Identification of Trends into Dose Calculations for Astronauts through Performing Sensitivity Analysis on Calculational Models Used by the Radiation Health Office

The Radiation Health Office (RHO) determines each astronaut s cancer risk by using models to associate the amount of radiation dose that astronauts receive from spaceflight missions. The baryon transport codes (BRYNTRN), high charge (Z) and energy transport codes (HZETRN), and computer risk models are used to determine the effective dose received by astronauts in Low Earth orbit (LEO). This code uses an approximation of the Boltzman transport formula. The purpose of the project is to run this code for various International Space Station (ISS) flight parameters in order to gain a better understanding of how this code responds to different scenarios. The project will determine how variations in one set of parameters such as, the point of the solar cycle and altitude can affect the radiation exposure of astronauts during ISS missions. This project will benefit NASA by improving mission dosimetry.

Adams, Thomas↗
NASA NTRS1980

Loss model for off-design performance analysis of radial turbines with pivoting-vane, variable-area stators

An off-design performance loss model for a radial turbine with pivoting, variable-area stators is developed through a combination of analytical modeling and experimental data analysis. A viscous loss model is used for the variation in stator loss with setting angle, and stator vane end-clearance leakage effects are predicted by a clearance flow model. The variation of rotor loss coefficient with stator setting angle is obtained by means of an analytical matching of experimental data for a rotor that was tested with six stators, having throat areas from 20 to 144% of the design area. An incidence loss model is selected to obtain best agreement with experimental data. The stator vane end-clearance leakage model predicts increasing mass flow and decreasing efficiency as a result of end-clearances, with changes becoming significantly larger with decreasing stator area.

Meitner, P. L.↗
DOE OSTI2022

Stochastic Model for High Temperature Oxidation of Cr–Ni Austenitic Steels Assisted by Spallation

Abstract Cr–Ni austenitic steels offer significant high temperature corrosion protection by forming a surface oxide layer. However, above critical service conditions (temperature, atmosphere, thermal cycling), oxidized surface can experience intensive degradation because of scale spallation, which could be detrimental to the in-service life. To predict the effect of scale spallation on oxidation kinetics, a simulation was implemented using a stochastic model. The model considers topological parameters and intensity of spallation which can occur, while delivering a true oxidation constant. The experimental procedure identified the amount of formed spalled scale and topology of spallation based on the use of element mapping of the surface. This information was used to determine a true kinetic constant for a corresponding spallation intensity in oxidized Cr–Ni austenitic steel. To illustrate the capability of the stochastic model, a parametric analysis was performed. The model verified how the spallation parameters could change the oxidation processes from parabolic growth of an adhered oxide layer without spallation to a mixed linear-parabolic, or with a constant thickness of residual scale at high spallation intensity. The spallation model will be used in a separate article to characterize high temperature surface degradation of several Cr–Ni austenitic steels during harsh oxidation environments.

36 MATERIALS SCIENCE↗
NASA NTRS2002

A model for plant lighting system selection

A decision model is presented that compares lighting systems for a plant growth scenario and chooses the most appropriate system from a given set of possible choices. The model utilizes a Multiple Attribute Utility Theory approach, and incorporates expert input and performance simulations to calculate a utility value for each lighting system being considered. The system with the highest utility is deemed the most appropriate system. The model was applied to a greenhouse scenario, and analyses were conducted to test the model's output for validity. Parameter variation indicates that the model performed as expected. Analysis of model output indicates that differences in utility among the candidate lighting systems were sufficiently large to give confidence that the model's order of selection was valid.

NASA Center KSC↗
NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗
NASA NTRS1980

Loss model for off-design performance analysis of radial turbines with pivoting-vane, variable-area stators

An off-design performance loss model is developed for variable-area (pivoted vane) radial turbines. The variation in stator loss with stator area is determined by a viscous loss model while the variation in rotor loss due to stator area variation (for no stator end-clearance gap) is determined through analytical matching of experimental data. An incidence loss model is also based on matching of the experimental data. A stator vane end-clearance leakage model is developed and sample calculations are made to show the predicted effects of stator vane end-clearance leakage on performance.

Meitner, P. L.↗
NASA NTRS2009

Evaluating Effectiveness of Modeling Motion System Feedback in the Enhanced Hess Structural Model of the Human Operator

In order to use the Hess Structural Model to predict the need for certain cueing systems, George and Cardullo significantly expanded it by adding motion feedback to the model and incorporating models of the motion system dynamics, motion cueing algorithm and a vestibular system. This paper proposes a methodology to evaluate effectiveness of these innovations by performing a comparison analysis of the model performance with and without the expanded motion feedback. The proposed methodology is composed of two stages. The first stage involves fine-tuning parameters of the original Hess structural model in order to match the actual control behavior recorded during the experiments at NASA Visual Motion Simulator (VMS) facility. The parameter tuning procedure utilizes a new automated parameter identification technique, which was developed at the Man-Machine Systems Lab at SUNY Binghamton. In the second stage of the proposed methodology, an expanded motion feedback is added to the structural model. The resulting performance of the model is then compared to that of the original one. As proposed by Hess, metrics to evaluate the performance of the models include comparison against the crossover models standards imposed on the crossover frequency and phase margin of the overall man-machine system. Preliminary results indicate the advantage of having the model of the motion system and motion cueing incorporated into the model of the human operator. It is also demonstrated that the crossover frequency and the phase margin of the expanded model are well within the limits imposed by the crossover model.

Zaychik, Kirill↗
DOE OSTI2024

Modelling and analysis of nuclear reactor system coupled with a liquid metal battery

Traditionally, nuclear power plants in the U.S. provide baseload power to the power grid because they have less flexibility for ramping their output power than natural gas peaking plants. However, achieving climate goals to reduce the consumption of fossil‐based natural gas places pressure on nuclear power plants and other power generators to ramp up their power output to balance grid generation with demand. This paper presents the modelling and performance analysis of a nuclear reactor system (NRS) coupled to a liquid‐metal battery (LMB) to improve its dynamic response and enable its black start capability. The NRS and LMB thermal behaviour are modelled in Dymola, while the electrical dynamics of the LMB and power grid are modelled in RTDS‐RSCAD. Both simulation platforms are coupled and share their thermal and electrical data using a Transmission Control Protocol/Internet Protocol (TCP/IP) communication protocol. The dynamic performance of the NRS‐LMB integration is tested on the IEEE 9 bus, which demonstrates its ability to respond and provide frequency and voltage regulation. The black start capability of the NRS‐LMB is also evaluated by simulating a grid outage and using the LMB to supply the auxiliary loads required to bring the NRS back online as soon as possible. The results show that coupling an NRS to an LMB improves the system dynamic performance and enables it to black start after being disconnected from the grid for several days.

25 ENERGY STORAGE↗
DOE OSTI2021

Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.13 Theory Manual

The Dakota toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantification, and optimization under uncertainty that provide the foundation for many of Dakota's iterative analysis capabilities.

97 MATHEMATICS AND COMPUTING↗