Search NASA⌕ Search

SEARCH · Search NASA

Results for “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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational Fluid Dynamic Modeling of Dry Cask Simulator with Crosswind

The purpose of this study is to create a STAR-CCM+ model of a Belowground Vertical Dry Cask Simulator (BVDCS) at Sandia National Laboratories (SNL) and validate the model with SNL’s experimental results. The BVDCS consists of a single boiling water reactor assembly fitted with electric heaters encompassed by a containment vessel and shell to represent a belowground spent nuclear fuel (SNF) dry storage system. Blowers are located near the inlet and outlet of the BVDCS to simulate crosswind conditions. In addition to the experimental results, the STAR-CCM+ model developed for this study is compared with a previous computational fluid dynamics (CFD) model in a different software program, which is used as a software-to-software benchmark. The experimental results provide a dataset to compare the STAR-CCM+ model results for a variety of different conditions. The main objective is to validate and improve STAR-CCM+ CFD models for spent nuclear fuel storage systems with explicitly modeled external environments and “wind driven” crossflows. These CFD models aide in the study of external particle deposition in spent nuclear fuel storage systems, which is important to predicting the significance of chloride induced stress corrosion cracking (CISCC). In addition to experimental comparison, a sensitivity analysis study is performed using the STAR-CCM+ model. The sensitivity analysis provides a quantitative assessment of the sensitivity of various parameters. This helps provide information on various parameters that are of particular importance to constructing a model representative of real life systems. The STAR-CCM+ model compared well to the experimental results showing similar responses to changes in cross wind flow, and a number of parameters are identified for model improvement.

Jensen, Ben J.↗
DOE OSTI2023

Sensitivity-Driven Experimental Design to Facilitate Control of Dynamical Systems

Control of nonlinear dynamical systems is a complex and multifaceted process. Essential elements of many engineering systems include high-fidelity physics-based modeling, offline trajectory planning, feedback control design, and data acquisition strategies to reduce uncertainties. Here this article proposes an optimization-centric perspective which couples these elements in a cohesive framework. We introduce a novel use of hyper-differential sensitivity analysis to understand the sensitivity of feedback controllers to parametric uncertainty in physics-based models used for trajectory planning. These sensitivities provide a foundation to define an optimal experimental design which seeks to acquire data most relevant in reducing demand on the feedback controller. Our proposed framework is illustrated on the Zermelo navigation problem and a hypersonic trajectory control problem using data from NASA’s X-43 hypersonic flight tests.

42 ENGINEERING↗
DOE OSTI2016

Evaluation of multiple reduced-order models to enhance confidence in global sensitivity analyses

Variance-based global sensitivity analysis (e.g., the Sobol' sensitivity index) can be used to identify the important parameters over the entire parameter space. However, one often cannot afford the computational costs of sampling-based approaches in combination with expensive high-fidelity forward models. Reduced-order models (ROM) can substantially accelerate calculation of these sensitivities. However, it is usually difficult to determine what type of ROM should be used and how accurately the ROM represents the high-fidelity model (HFM) results. Here in this paper, we propose to concurrently use multiple ROMs as a way to assess the robustness of the model-reduction method. Two sets of HFM simulations are needed, one set for building ROMs and the other for validating ROMs. Our goal is to keep the total number of HFM simulations to a minimum. Ideally some of the HFM simulations in the first set can be shared by different ROMs. Based on validation results, the ROMs can be combined with different schemes. We demonstrate that we can achieve the goal by using four different ROMs and still considerably save computational time compared to using traditional HFM simulation for calculating sensitivity indices. We apply the approach to an example problem of a large-scale geological carbon dioxide storage system, in which the objective is to calculate a sensitivity index to identify important parameters. For this problem, the locally best ROM provides better estimates than the weighted average from all ROMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

A Comprehensive View of Global Potential for Hydro-generated Electricity

In this study, we assess global hydropower potential using runoff and stream flow data, along with turbine technology performance, cost assumptions, and environmental considerations. The results provide the first comprehensive quantification of global hydropower potential including gross, technical, economic, and exploitable estimates. Total global potential of gross, technical, economic, and exploitable hydropower are estimated to be approximately 128, 39, 32, and 27 petawatt hours per year, respectively. The economic and exploitable potential of hydropower are calculated at less than 9 cents/kWh. We find that hydropower has the potential to supply a significant portion of the world energy needs, although this potential varies substantially by region. Globally, hydropower can potentially supply about 1.5 times the total electricity demand in 2005. Estimated hydropower resources in a number of countries are sufficient to accommodate their demand for electricity in 2005, e.g., Brazil (5.6 times), Russia (4.6 times), and Canada (3.5 times). A sensitivity analysis indicates that hydropower estimates are not highly sensitive to five key parameters: design flow (varying by -2% to +1% at less than 9 cents/kWh), cost and financing options (by -7% to +6%), turbine efficiency (by -10% to +10%), stream flow (by -10% to +10%), and fixed charge rate (by -6% to 5%). This sensitivity analysis emphasizes the reliable role of hydropower for future energy systems, when compared to other renewable energy resources with larger uncertainty in their future potentials.

Zhou, Yuyu↗
DOE OSTI2016

iTOUGH2 v7.1

iTOUGH2 (inverse TOUGH2) provides inverse modeling capabilities for TOUGH2, a simulator for multi-dimensional, multi-phase, multi-component, non-isothermal flow and transport in fractured porous media. iTOUGH2 performs sensitivity analyses, data-worth analyses, parameter estimation, and uncertainty propagation analyses in geosciences and reservoir engineering and other application areas. iTOUGH2 supports a number of different combinations of fluids and components (equation-of-state (EOS) modules). In addition, the optimization routines implemented in iTOUGH2 can also be used for sensitivity analysis, automatic model calibration, and uncertainty quantification of any external code that uses text-based input and output files using the PEST protocol. iTOUGH2 solves the inverse problem by minimizing a non-linear objective function of the weighted differences between model output and the corresponding observations. Multiple minimization algorithms (derivative-free, gradient-based, and second-order; local and global) are available. iTOUGH2 also performs Latin Hypercube Monte Carlo simulations for uncertainty propagation analyses. A detailed residual and error analysis is provided. This upgrade includes (a) global sensitivity analysis methods, (b) dynamic memory allocation (c) additional input features and output analyses, (d) increased forward simulation capabilities, (e) parallel execution on multicore PCs and Linux clusters, and (f) bug fixes.

Finsterle, Stefan↗
DOE OSTI2017

Development of a Multiphase Adjoint Capability in OpenFOAM

Thermal hydraulic predictions of flow regimes within nuclear reactor cores require significant computational resources and accuracy to ensure that the core design does not violate the thermal limits of materials. Multiphase computational fluid dynamics (CFD) has become a valuable computational capability for predicting flow characteristics as they relate to applications such as nuclear reactor safety and mixing phenomena around spacer grids within fuel assemblies. Multiphase CFD requires closure relationships for both the effects of turbulence as well as the transfer of mass, momentum, and energy between phases. Understanding the sensitivity of the parameters used in closure relationships is important for improving accuracy, but performing rigorous sensitivity analysis is computationally expensive. Adjoint mathematics have been utilized for reactor physics and fluid dynamics applications with respect to verification, sensitivity analysis, adaptive mesh refinement, and optimization. The usage of adjoint methods to predict the sensitivities of a variety of closure relationship parameters within a given problem has been shown to return sensitivity responses with significantly fewer computations than normal 'large-scale' methods. As the complexity of multiphase CFD methods increases, the demand for accurate verification tools and improvements in computational efficiency also increases, making the development of adjoint capabilities an attractive option. Closure relationships that describe interphase momentum physics in multiphase CFD modelling rely heavily on prescribed constants determined from experimental data. Due to the computational efficiency of an adjoint approach, the time to derive these closure relationships could be dramatically reduced, and data assimilation techniques could be used to return probability density functions of closure models derived using experimental data. This work develops a multiphase adjoint capability for the Eulerian-Eulerian two field, two phase equations. Specifically, the adjoint capability has been developed for the sub-cooled nucleate boiling solver boilEulerFoam developed by Dr. Nam Dinh of NC State University within the open-source CFD library OpenFOAM.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Arecibo pulsar survey using ALFA. IV. Mock spectrometer data analysis, survey sensitivity, and the discovery of 40 pulsars

The on-going Arecibo Pulsar-ALFA (PALFA) survey began in 2004 and is searching for radio pulsars in the Galactic plane at 1.4 GHz. Here we present a comprehensive description of one of its main data reduction pipelines that is based on the PRESTO software and includes new interference-excision algorithms and candidate selection heuristics. This pipeline has been used to discover 40 pulsars, bringing the survey’s discovery total to 144 pulsars. Of the new discoveries, eight are millisecond pulsars (MSPs; P<10 ms) and one is a Fast Radio Burst (FRB). This pipeline has also re-detected 188 previously known pulsars, 60 of them previously discovered by the other PALFA pipelines. We present a novel method for determining the survey sensitivity that accurately takes into account the effects of interference and red noise: we inject synthetic pulsar signals with various parameters into real survey observations and then attempt to recover them with our pipeline. We find that the PALFA survey achieves the sensitivity to MSPs predicted by theoretical models but suffers a degradation for P≳100 ms that gradually becomes up to ∼10 times worse for P>4 s at DM<150 pc cm{sup −3}. We estimate 33 ± 3% of the slower pulsars are missed, largely due to red noise. A population synthesis analysis using the sensitivity limits we measured suggests the PALFA survey should have found 224 ± 16 un-recycled pulsars in the data set analyzed, in agreement with the 241 actually detected. The reduced sensitivity could have implications on estimates of the number of long-period pulsars in the Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Development of a Stochastic Infiltration Model for PPA Implementation - 19293

Performance Assessment models often require inputs representing specific hydrologic processes and properties such as infiltration, water content, groundwater flow rate, streamflow, and sediment flux. This paper details the development of a process-level infiltration model and its use in a Probabilistic Performance Assessment (PPA) model for the West Valley Site in western New York. Additionally, this paper demonstrates a method for obtaining distributions of the long-term average hydrologic processes and properties for a PA model. For this PPA, an infiltration model was developed using HELP (Hydrologic Evaluation of Landfill Performance) to evaluate the rates of infiltration, percolation, and drainage through several site soil and proposed decommissioning cover systems, as well as the volumetric water contents of each of the unsaturated layers in a modeled soil column. A particular challenge of probabilistic modeling over long periods of time is scaling parameters appropriately so that outcomes are not grossly over- or underestimated. For these models, distributions of climatic parameters such as average precipitation rate, temperature, wind speed, humidity, and solar radiation were scaled to 1000-year averages and were used to force steady-state models that predict 1000-year average infiltration rates, drainage rates, and water contents. For the HELP model, distributions were developed for the physical and hydrologic properties of each material in a given soil column or cover system, and these properties were allowed to vary stochastically along with climatic inputs. The HELP model was run 5000 times for each of seven different soil and cover systems representing various existing and proposed conditions (i.e. native soil, backfill, existing covers, and a proposed close-in-place cover). The results of the HELP model provide ranges of 1000-year average infiltration rates, drainage rates, and water contents through and within the various layers of each of the different soil and cover conditions. The model outcomes also demonstrate the effectiveness of a proposed close-in-place cover, which essentially eliminates infiltration during its operational lifetime. Following model simulation, a sensitivity analysis was performed to establish the relative importance of each stochastic input with respect to each output. The outcomes of the model simulations and the sensitivity analysis were implemented into a system-level GoldSim model via a novel sensitivity-informed nearest neighbor approach. These infiltration rates, drainage rates, and water contents are used to parameterize mixing cells within the GoldSim Contaminant Transport module, which is the primary system-level model used in the PPA. The infiltration rates are also used as boundary conditions for a stochastic groundwater flow model that is also utilized in the PPA, but not discussed in this paper. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

ASME V\&V challenge problem: Surrogate-based V&V

The process of verification and validation can be resource intensive. From the computational model perspective, the resource demand typically arises from long simulation run times on multiple cores coupled with the need to characterize and propagate uncertainties. In addition, predictive computations performed for safety and reliability analyses have similar resource requirements. For this reason, there is a tradeoff between the time required to complete the requisite studies and the fidelity or accuracy of the results that can be obtained. At a high level, our approach is cast within a validation hierarchy that provides a framework in which we perform sensitivity analysis, model calibration, model validation, and prediction. The evidence gathered as part of these activities is mapped into the Predictive Capability Maturity Model to assess credibility of the model used for the reliability predictions. With regard to specific technical aspects of our analysis, we employ surrogate-based methods, primarily based on polynomial chaos expansions and Gaussian processes, for model calibration, sensitivity analysis, and uncertainty quantification in order to reduce the number of simulations that must be done. The goal is to tip the tradeoff balance to improving accuracy without increasing the computational demands.

42 ENGINEERING↗
DOE OSTI2019

Multiscale modeling of fission gas behavior in U 3 Si 2 under LWR conditions

In this work, we present a model of fission gas behavior in U 3 Si 2 under light water reactor (LWR) conditions for application in engineering fuel performance codes. The model includes components for intra-granular and inter-granular behavior of fission gases. The intra-granular component is based on cluster dynamics and computes the evolution of intra-granular fission gas bubbles and swelling coupled to gas diffusion to grain boundaries. The inter-granular component describes the evolution of grain-boundary fission gas bubbles coupled to fission gas release. Given the lack of experimental data for U 3 Si 2 under LWR conditions, the model is informed with parameters calculated via atomistic simulations. In particular, we derive fission gas diffusivities through density functional theory calculations, and the re-solution rate of fission gas atoms from intra-granular bubbles through binary collision approximation calculations. Further, the developed model is applied to the simulation of an experiment for U 3 Si 2 irradiated under LWR conditions available from the literature. Results point out a credible representation of fission gas swelling and release in U 3 Si 2 . Finally, we perform a sensitivity analysis for the various model parameters. Based on the sensitivity analysis, indications are derived that can help in addressing future research on the characterization of the physical parameters relative to fission gas behavior in U 3 Si 2 . The developed model is intended to provide a suitable infrastructure for the engineering scale calculation of fission gas behavior in U 3 Si 2 that exploits a multiscale approach to fill the experimental data gap and can be progressively improved as new lower-length scale calculations and validation data become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1991

Jet-A reaction mechanism study for combustion application

Simplified chemical kinetic reaction mechanisms for the combustion of Jet A fuel was studied. Initially, 40 reacting species and 118 elementary chemical reactions were chosen based on a literature review. Through a sensitivity analysis with the use of LSENS General Kinetics and Sensitivity Analysis Code, 16 species and 21 elementary chemical reactions were determined from this study. This mechanism is first justified by comparison of calculated ignition delay time with the available shock tube data, then it is validated by comparison of calculated emissions from the plug flow reactor code with in-house flame tube data.

Lee, Chi-Ming↗
NASA NTRS1991

Jet-A reaction mechanism study for combustion application

Simplified chemical kinetic reaction mechanisms for the combustion of Jet A fuel are studied. Initially 40 reacting species and 118 elementary chemical reactions were chosen based on the literature review of previous works. Through a sensitivity analysis with the use of LSENS General Kinetics and Sensitivity Analysis Code, 16 species and 21 elementary chemical reactions were determined from this study. This mechanism is first justified by comparison of calculated ignition delay time with available shock tube data, then it is validated by comparison of calculated emissions from plug flow reactor code with in-house flame tube data.

Lee, Chi-Ming↗
DOE OSTI2017

Adjoint-Based Sensitivity and Uncertainty Analysis for Density and Composition: A User’s Guide

The ability to perform sensitivity analyses using adjoint-based first-order sensitivity theory has existed for decades. This paper provides guidance on how adjoint sensitivity methods can be used to predict the effect of material density and composition uncertainties in critical experiments, including when these uncertain parameters are correlated or constrained. Two widely used Monte Carlo codes, MCNP6 (Ref. 2) and SCALE 6.2 (Ref. 3), are both capable of computing isotopic density sensitivities in continuous energy and angle. Additionally, Perkó et al. have shown how individual isotope density sensitivities, easily computed using adjoint methods, can be combined to compute constrained first-order sensitivities that may be used in the uncertainty analysis. This paper provides details on how the codes are used to compute first-order sensitivities and how the sensitivities are used in an uncertainty analysis. Constrained first-order sensitivities are computed in a simple example problem.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Demonstration of non-destructive and isotope-sensitive material analysis using a short-pulsed laser-driven epi-thermal neutron source

Neutrons are a valuable tool for non-destructive material investigation as their interaction cross sections with matter are isotope sensitive and can be used complementary to x-rays. So far, most neutron applications have been limited to large-scale facilities such as nuclear research reactors, spallation sources, and accelerator-driven neutron sources. Here we show the design and optimization of a laser-driven neutron source in the epi-thermal and thermal energy range, which is used for non-invasive material analysis. Neutron resonance spectroscopy, neutron radiography, and neutron resonance imaging with moderated neutrons are demonstrated for investigating samples in terms of isotope composition and thickness. The experimental results encourage applications in non-destructive and isotope-sensitive material analysis and pave the way for compact laser-driven neutron sources with high application potential.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Validation and Parametric Investigations Using a Lumped Thermal Parameter Model of an Internal Permanent Magnet Motor

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet (IPM) synchronous motor within the commercially available Motor-CAD® modeling environment. The IPM motor’s stator is studied at steady state, and winding losses are generated by a constant DC current. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% — for the highest temperature in the slot windings and the end windings — was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot windings and end windings. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance. The latter is often ignored in the literature, whereas its impact on temperature rise was found to be more significant than any other contact resistance within the stator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Validation and Parametric Investigations Using a Lumped Thermal Parameter Model of an Internal Permanent Magnet Motor: Preprint

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multi-physics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a Lumped Parameter Thermal Network (LPTN) model of an Internal Permanent Magnet (IPM) synchronous motor within the commercially available Motor-CAD® modeling environment. The IPM motor’s stator is studied at steady state, and winding losses are generated by a constant DC current. The validation is based on temperature comparison with experimental data and with more detailed Finite Element Analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot windings and the end windings - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot windings and end windings. As for the LTPN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance. The latter is often ignored in the literature, whereas its impact on temperature rise was found to be more significant than any other contact resistance within the stator.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multiphysics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a lumped parameter thermal network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available MOTOR-CAD modeling environment. The validation is based on temperature comparison with experimental data and with more detailed finite element analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations, and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4% - for the highest temperature in the slot-winding and the end-winding - was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LPTN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

42 ENGINEERING↗
DOE OSTI2016

An integrated approach for the knowledge discovery in computer simulation models with a multi-dimensional parameter space

In production industries, parameter identification, sensitivity analysis and multi-dimensional visualization are vital steps in the planning process for achieving optimal designs and gaining valuable information. Sensitivity analysis and visualization can help in identifying the most-influential parameters and quantify their contribution to the model output, reduce the model complexity, and enhance the understanding of the model behavior. Typically, this requires a large number of simulations, which can be both very expensive and time consuming when the simulation models are numerically complex and the number of parameter inputs increases. There are three main constituent parts in this work. The first part is to substitute the numerical, physical model by an accurate surrogate model, the so-called metamodel. The second part includes a multi-dimensional visualization approach for the visual exploration of metamodels. In the third part, the metamodel is used to provide the two global sensitivity measures: i) the Elementary Effect for screening the parameters, and ii) the variance decomposition method for calculating the Sobol indices that quantify both the main and interaction effects. The application of the proposed approach is illustrated with an industrial application with the goal of optimizing a drilling process using a Gaussian laser beam.

97 MATHEMATICS AND COMPUTING↗