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Results for “global sensitivity analysis”

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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Visualization of Global Sensitivity Analysis Results Based on a Combination of Linearly Dependent and Independent Directions

A useful technique for the validation and verification of complex flight systems is Monte Carlo Filtering -- a global sensitivity analysis that tries to find the inputs and ranges that are most likely to lead to a subset of the outputs. A thorough exploration of the parameter space for complex integrated systems may require thousands of experiments and hundreds of controlled and measured variables. Tools for analyzing this space often have limitations caused by the numerical problems associated with high dimensionality and caused by the assumption of independence of all of the dimensions. To combat both of these limitations, we propose a technique that uses a combination of the original variables with the derived variables obtained during a principal component analysis.

Davies, Misty D.↗
NASA NTRS2024

Global Sensitivity Analysis of Simulated Remote Sensing Polarimetric Observations Over Snow

This study presents a detailed theoretical assessment of the information content of passive polarimetric observations over snow scenes, using a global sensitivity analysis (GSA) method. Conventional sensitivity studies focus on varying a single parameter while keeping all other parameters fixed. In contrast, the GSA correctly addresses the covariance of state parameters across their entire parameter space, hence favoring a more correct interpretation of inversion algorithms and the optimal design of their state vectors. The forward simulations exploit a vector radiative transfer model to obtain the Stokes vector emerging at the top of the atmosphere for different solar zenith angles, when the bottom boundary consists of a vertically resolved snowpack of non-spherical grains. The presence of light-absorbing particulates (LAPs), either embedded in the snow or aloft in the atmosphere above in the form of aerosols, is also considered. The results are presented for a set of wavelengths spanning the visible (VIS), near-infrared (NIR), and shortwave infrared (SWIR) region of the spectrum. The GSA correctly captures the expected, high sensitivity of the reflectance to LAPs in the VIS–NIR and to grain size at different depths in the snowpack in the NIR–SWIR. With adequate viewing geometries, mono-angle measurements of total reflectance in the VIS–SWIR (akin to those of the Moderate Resolution Imaging Spectroradiometer, MODIS) resolve grain size in the top layer of the snowpack sufficiently well. The addition of multi-angle polarimetric observations in the VIS–NIR provides information on grain shape and microscale roughness. The simultaneous sensitivity in the VIS–NIR to both aerosols and snow-embedded impurities can be disentangled by extending the spectral range to the SWIR, which contains information on aerosol optical depth while remaining essentially unaffected when the same particulates are mixed with the snow. Multi-angle polarimetric observations can therefore (i) effectively partition LAPs between the atmosphere and the surface, which represents a notorious challenge for snow remote sensing based on measurements of total reflectance only and (ii) lead to better estimates of grain shape and roughness and, in turn, the asymmetry parameter, which is critical for the determination of albedo. The retrieval uncertainties are minimized when the degree of linear polarization is used in place of the polarized reflectance. The Sobol indices, which are the main metric for the GSA, were used to select the state parameters in retrievals performed on data simulated for multiple instrument configurations. Improvements in retrieval quality with the addition of measurements of polarization, multi-angle views, and different spectral channels reflect the information content, identified by the Sobol indices, relative to each configuration. The results encourage the development of new remote sensing algorithms that fully leverage multi-angle and polarimetric capabilities of modern remote sensors. They can also aid flight planning activities, since the optimal exploitation of the information content of multi-angle measurements depends on the viewing geometry. The better characterization of surface and atmospheric parameters in snow-covered regions advances research opportunities for scientists of the cryosphere and ultimately benefits albedo estimates in climate models.

remote sensing↗
NASA NTRS1989

Global sensitivity analysis in control-augmented structural synthesis

In this paper, an integrated approach to structural/control design is proposed in which variables in both the passive (structural) and active (control) disciplines of an optimization process are changed simultaneously. The global sensitivity equation (GSE) method of Sobieszczanski-Sobieski (1988) is used to obtain the behavior sensitivity derivatives necessary for the linear approximations used in the parallel multidisciplinary synthesis problem. The GSE allows for the decoupling of large systems into smaller subsystems and thus makes it possible to determine the local sensitivities of each subsystem's outputs to its inputs and parameters. The advantages in using the GSE method are demonstrated using a finite-element representation of a truss structure equipped with active lateral displacement controllers, which is undergoing forced vibration.

Bloebaum, Christina L.↗
NASA NTRS1989

Global, seasonal cloud variations from satellite radiance measurements. I - Sensitivity of analysis

Global, daily, visible and IR radiance measurements from the NOAA-5 Scanning Radiometer (SR) are analyzed for the months of January, April, July and October, 1977 to infer cloud and surface radiative properties. In this paper, the data and analysis method are described. A unique feature of the method is that it utilizes radiative transfer models that simulate the SR measurements using explicit parameters representing the properties of the surface, atmosphere, and clouds. The accuracy of all the results depends primarily on the proper separation of the total radiance distribution into those parts representing clear and cloudy scenes.

Rossow, William B.↗
NASA NTRS2016

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

Uncertainty↗
NASA NTRSDate not supplied

Uncertainty Quantification and Sensitivity Analysis in Process-Structure-Property Simulations for Laser Powder Bed Fusion Additive Manufacturing

Process variations and process-induced defects like porosity cause significant uncertainty in the microstructure and mechanical behavior of additively manufactured metals. Establishing process-structure-property (PSP) relationships and quantifying uncertainty using experiments alone is costly, especially for structural applications where mechanical allowables must be established for qualification and certification. This work presents a PSP simulation framework for laser powder bed fusion with a focus on uncertainty quantification through probabilistic calibration and multi-fidelity uncertainty propagation. Motivated by phenomenological input parameters related to grain nucleation and growth that are difficult to characterize, a global sensitivity analysis (GSA) is completed. Through GSA, the most important input parameters are identified based on their influence on the statistical distributions of microstructural metrics that influence mechanical behavior, including grain size, morphology, and crystallographic texture. The results provide insight on what experiments are necessary to quantify and control PSP uncertainties, particularly those associated with the more challenging input parameters.

additive manufacturing↗
NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗
NASA NTRSDate not supplied

Exploitation of a Validation Hierarchy for Modeling and Simulation

Across engineering there is an evolving need to increase reliance on physics-based simulation to develop, design and optimize engineering systems. This increased reliance on modeling and simulation has highlighted a growing need to transform the confidence that modeling and simulation analysts have in their results into credibility for systems engineers to design and field systems more quickly and with less physical testing. For isolated components of a complex system, where a single discipline may drive product design, this is less of a concern as the relationship is often straightforward and easy to explain. However, when these isolated components are integrated, and are expected to operate in a multi-disciplinary context in which safety critical systems are involved, new concepts and model assurance standards are required. In this paper we address this challenge by showing how a model validation hierarchy can be exploited to identify those model validation experiments that will contribute most to increasing confidence and credibility of modeling and simulation predictions. The approach that is adopted contains four main steps. The first step is the construction of a model validation hierarchy that links subsystems, assemblies, and components to a hierarchy of physical experiments that can be used support model validation. This hierarchy connects the concerns of systems engineers to those of the modeling and simulation analyst in a clear and logical way. The structure and content of this hierarchy is then used in a second step to establish which physical phenomena have the greatest impact on overall system performance metrics. A gap analysis technique, based upon modeling and simulation concerns, is then used to prioritize the important physical phenomenon. Unfortunately, a common outcome of such gap analyses is the identification of many important gaps and so, in the final step of our process, we advocate the use of a global sensitivity analysis as a means to complete the prioritization.

Verification and Validation↗
NASA NTRS2019

Uncertainty Quantification of a Rotorcraft Conceptual Sizing Toolsuite

A computational framework to support the quantification of system uncertainties and sensitivities for rotorcraft applications is presented using the NASA Design and Analysis of Rotorcraft (NDARC) conceptual sizing tool. A 90 passenger conceptual tiltrotor configuration was used for case demonstration in the modeling of uncertainties in NDARCs emission module. A non-intrusive forward propagation uncertainty quantification approach was applied to ensemble simulations using a Monte Carlo methodology with stratified Latin hypercube sampling. An off-the-shelf software, DAKOTA, which supports trade studies and design space exploration, including optimization, surrogate modeling and uncertainty analysis was used to address the research goals. A toolsuite was further developed incorporating DAKOTA with automated design processes and methods using function wrappers to execute program routines including support for data post-processing. Uncertainties in rotorcraft emissions modeling using the Average Temperature Response metric for a set mission profile were studied. It was shown that for the current study, using the base-line best estimate modeling parameters for the Average Temperature Response metric, NDARC under-estimates the effects of emissions when compared with results from Monte Carlo simulations. A global sensitivity analysis was further undertaken to quantify the contribution of the various emission species on output sensitivity, hence uncertainty. The work demonstrates that the developed toolsuite is robust and will support the quantification of system uncertainties and sensitivities in future rotorcraft design efforts.

Rotorcraft↗
NASA NTRS2010

The Effect of Nondeterministic Parameters on Shock-Associated Noise Prediction Modeling

Engineering applications for aircraft noise prediction contain models for physical phenomenon that enable solutions to be computed quickly. These models contain parameters that have an uncertainty not accounted for in the solution. To include uncertainty in the solution, nondeterministic computational methods are applied. Using prediction models for supersonic jet broadband shock-associated noise, fixed model parameters are replaced by probability distributions to illustrate one of these methods. The results show the impact of using nondeterministic parameters both on estimating the model output uncertainty and on the model spectral level prediction. In addition, a global sensitivity analysis is used to determine the influence of the model parameters on the output, and to identify the parameters with the least influence on model output.

Dahl, Milo D.↗
NASA NTRSDate not supplied

Validation and Sensitivity Analyses of Arc-Jet Performance of Woven Thermal Protection Entry Systems

A material response model is developed for the determination of erosion rates and thermal response of woven materials to enable accurate thermal protection material sizing and future missions to Venus and giant planets. The stagnation-point thermal response is further evaluated using a Monte Carlo global sensitivity analysis to determine the interactions between key parameters in the material surface energy balance, such as, convective blowing correction parameter and weave material properties including isotropic and orthotropic thermal conductivites. The time-accurate material response for the dual layer weave erosion and surface temperatures show excellent agreement with stagnation flat face arc-jet tests involving time-varying aeroheating-cooling cycles. The high-fidelity Monte Carlo sensitivity analysis technique was used to address challenges in thermal protection sizing for the Adaptive Deployable Entry and Placement Technology (ADEPT) system, a low ballistic coefficient hypersonic decelerator. The results represent new correlations for such weaves involving Carbon-air oxidation equilibrium chemistry. Through the multi-variate regression analyses and uncertainty rankings performed, only three properties were found to contribute to the transient thermal response of the gore, with emissivity contributing to nearly 95% of the total output uncertainty.

Pratibha Raghunandan↗
NASA NTRS2021

Validation and Sensitivity Analyses of Arc-Jet Performance of Woven Thermal Protection Entry Systems

A material response model is developed for the determination of erosion rates and thermal response of woven materials to enable accurate thermal protection material sizing and future missions to Venus and giant planets. The stagnation-point thermal response is further evaluated using a Monte Carlo global sensitivity analysis to determine the interactions between key parameters in the material surface energy balance, such as, convective blowing correction parameter and weave material properties including isotropic and orthotropic thermal conductivites. The time-accurate material response for the dual layer weave erosion and surface temperatures show excellent agreement with stagnation flat face arc-jet tests involving time-varying aeroheating-cooling cycles. The high-fidelity Monte Carlo sensitivity analysis technique was used to address challenges in thermal protection sizing for the Adaptive Deployable Entry and Placement Technology (ADEPT) system, a low ballistic coefficient hypersonic decelerator. The results represent new correlations for such weaves involving Carbon-air oxidation equilibrium chemistry. Through the multi-variate regression analyses and uncertainty rankings performed, only three properties were found to contribute to the transient thermal response of the gore, with emissivity contributing to nearly 95% of the total output uncertainty.

Pratibha Raghunandan↗
NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗
NASA NTRS1996

GLSENS: A Generalized Extension of LSENS Including Global Reactions and Added Sensitivity Analysis for the Perfectly Stirred Reactor

A generalized version of the NASA Lewis general kinetics code, LSENS, is described. The new code allows the use of global reactions as well as molecular processes in a chemical mechanism. The code also incorporates the capability of performing sensitivity analysis calculations for a perfectly stirred reactor rapidly and conveniently at the same time that the main kinetics calculations are being done. The GLSENS code has been extensively tested and has been found to be accurate and efficient. Nine example problems are presented and complete user instructions are given for the new capabilities. This report is to be used in conjunction with the documentation for the original LSENS code.

Bittker, David A.↗
NASA NTRS1983

Textural edge detection and sensitivity analysis

An optimum (global) textural edge detection operator based on statistical models for texture was developed. Also the effects of the imaging process on the textural patterns of a scene as it appears in the image were analyzed.

Shanmugan, K. S.↗
NASA NTRS1991

Sensitivity analysis of a wing aeroelastic response

A variation of Sobieski's Global Sensitivity Equations (GSE) approach is implemented to obtain the sensitivity of the static aeroelastic response of a three-dimensional wing model. The formulation is quite general and accepts any aerodynamics and structural analysis capability. An interface code is written to convert one analysis's output to the other's input, and visa versa. Local sensitivity derivatives are calculated by either analytic methods or finite difference techniques. A program to combine the local sensitivities, such as the sensitivity of the stiffness matrix or the aerodynamic kernel matrix, into global sensitivity derivatives is developed. The aerodynamic analysis package FAST, using a lifting surface theory, and a structural package, ELAPS, implementing Giles' equivalent plate model are used.

Kapania, Rakesh K.↗