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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Numerical Uncertainty Analysis for Computational Fluid Dynamics using Student T Distribution -- Application of CFD Uncertainty Analysis Compared to Exact Analytical Solution

Computational Fluid Dynamics (CFD) is the standard numerical tool used by Fluid Dynamists to estimate solutions to many problems in academia, government, and industry. CFD is known to have errors and uncertainties and there is no universally adopted method to estimate such quantities. This paper describes an approach to estimate CFD uncertainties strictly numerically using inputs and the Student-T distribution. The approach is compared to an exact analytical solution of fully developed, laminar flow between infinite, stationary plates. It is shown that treating all CFD input parameters as oscillatory uncertainty terms coupled with the Student-T distribution can encompass the exact solution.

Groves, Curtis E.↗
NASA NTRSDate not supplied

Practical Aspects of Multiple-Input Design for Aircraft System Identification Flight Tests

Practical aspects of multiple-input design methods for aircraft system identification flight tests are discussed. The orthogonal optimized multisine multiple-input design technique is explained and demonstrated, along with useful variations. Practical implementation and application issues associated with applying orthogonal optimized multisine inputs in flight are explained, and solutions are demonstrated using flight data from full-scale and subscale aircraft, as well as simulated flight data from an F-16 nonlinear simulation. Topics include truncated and extended excitation inputs, variations for control effectors with limited movement capabilities, aircraft dynamic response to excitation inputs, pilot implementation, statistical uncertainty quantification, multiple-input efficiency and effectiveness, flight testing with active feedback control, frequency-response estimation, control actuator modeling, and control interaction effects. Examples demonstrate that the orthogonal optimized multisine input design method is a practical, versatile, and effective approach for designing multiple excitation inputs for aircraft system identification flight tests.

Multiple-Input Design↗
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Advanced Graphite Creep Uncertainty Analysis

Radiation damage estimation is an important component of the post irradiation analysis of the Advanced Graphite Creep (AGC) experiment. It depends primarily on the fast fluence, which is determined using well established methods of spectral adjustment. These are based on best estimates from models such as Monte Carlo N-Particle (MCNP), input cross-sections, and measured activities from flux wires in the experiment. Each of these parameters can propagate uncertainties which will affect the uncertainty in the calculated dose levels for AGC, or any experiment irradiated within a reactor. While the methods of propagating uncertainty are well-established, the final uncertainty estimates they provide are only as good as the estimates of uncertainty in the inputs on which they are based. The purpose of this work is to outline some deficiencies in the ways these input uncertainties are presently estimated, and to outline a methodology by which they can be improved. The fast fluence and radiation damage received by graphite specimens irradiated in the Advanced Graphite Creep (AGC) experiments is presently estimated using spectral adjustment methods that are based on both flux wire activity measurements, and MCNP model predictions. This work describes an ongoing effort to quantify and propagate uncertainties in inputs to the spectral adjustment process, and thereby quantify the resultant error in radiation damage (dpa) estimates. The effort is multi-faceted, and we consider the impacts of both the set of flux wires selected, and the counting process. An expanded set of flux wires is identified that provides a more comprehensive data set on the fast spectrum. To address the counting process itself, a series of round-robin measurements in several reactor metrology laboratories across the Department of Energy (DOE) complex and nuclear industry are being undertaken to refine the American Society for Testing and Materials (ASTM) standards for flux wire measurements. To address the contribution of uncertainty in the MCNP model predictions, an uncertainty quantification (UQ) tool has been developed that statistically samples the model input parameters, runs a series of cases, and assimilates the results to provide an overall uncertainty. The impact of the MCNP UQ tool results is demonstrated by re-analyzing previous AGC flux wire and irradiation data. While the expanded flux wire set obviously cannot be added to these experiments retroactively, plans for future graphite irradiations are outlined.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2002

Problems in Assessment of the UV Penetration into Natural Waters from Space-based Measurements

Satellite instruments currently provide global maps of surface UV (ultraviolet) irradiance by combining backscattered radiance data with radiative transfer models. The models are often limited by uncertainties in physical input parameters of the atmosphere and surface. Global mapping of the underwater UV irradiance creates further challenges for the models. The uncertainties in physical input parameters become more serious because of the presence of absorbing and scattering quantities caused by biological processes within the oceans. In this paper we summarize the problems encountered in the assessment of the underwater UV irradiance from space-based measurements, and propose approaches to resolve the problems. We have developed a radiative transfer scheme for computation of the UV irradiance in the atmosphere-ocean system. The scheme makes use of input parameters derived from satellite instruments such as TOMS (Total Ozone Mapping Spectrometer) and SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The major problem in assessment of the surface UV irradiance is to accurately quantify the effects of clouds. Unlike the standard TOMS UV algorithm, we use the cloud fraction products available from SeaWiFS and MODIS (Moderate Resolution Imaging Spectrometer) to calculate instantaneous surface flux at the ocean surface. Daily UV doses can be calculated by assuming a model of constant cloudiness throughout the day. Both SeaWiFS and MODIS provide some estimates of seawater optical properties in the visible. To calculate the underwater UV flux the seawater optical properties must be extrapolated down to shorter wavelengths. Currently, the problem of accurate extrapolation of visible data down to the UV spectral range is not solved completely, and there are few available measurements. The major difficulty is insufficient correlation between photosynthetic and photoprotective pigments of phytoplankton absorbing in the visible and UV respectively. We propose to empirically parameterize seawater absorption in the UV on a basis of available data sets of bio-optical measurements from a variety of ocean waters. Another problem is the lack of reliable data on pure seawater absorption in the UV. Laboratory measurements of the UV absorption of both pure water and pure seawater are required.

Vasilkov, Alexander P.↗
NASA NTRS1990

A game theoretic controller for a linear time-invariant system with parameter uncertainty and its application to the Space Station

A game theoretic controller is developed for a linear time-invariant system with parameter uncertainties in system and input matrices. The input-output decomposition modeling for the plant uncertainty is adopted. The uncertain dynamic system is represented as an internal feedback loop in which the system is assumed forced by fictitious disturbance caused by the parameter uncertainty. By considering the input and the fictitious disturbance as two noncooperative players, a differential game problem is constructed. It is shown that the resulting time invariant controller stabilizes the uncertain system for a prescribed uncertainty bound. This game theoretic controller is applied to the momentum management and attitude control of the Space Station in the presence of uncertainties in the moments of inertia. Inclusion of the external disturbance torque to the design procedure results in a dynamical feedback controller which consists of conventional PID control and cyclic disturbance rejection filter. It is shown that the game theoretic design, comparing to the LQR design or pole placement design, improves the stability robustness with respect to inertia variations.

Rhee, Ihnseok↗
NASA NTRS1981

Density of the mantle of Mars

The range of zero pressure densities of the Martian mantle which are compatible with the mean density and moment of inertia factor of Mars has been explored by: (1) considering the full range of plausible core compositions and densities, and (2) investigating the sensitivity of computed mantle densities to uncertainties in the input parameters. Ni, Fe, Fe3O4, and FeS cores were considered; corresponding zero pressure mantle densities range from 3.47 to 3.41 g/cu cm. Uncertainty in the mantle density of about 0.04 g/cu cm for the Fe core model and about 0.05 g/cu cm for the FeS core model results from uncertainties in the input parameters. The zero pressure density of the Martian mantle is estimated to be 3.44 plus or minus 0.06 g/cu cm.

Goettel, K. A.↗
NASA NTRS1985

What have we learned from modeling giant planet interiors?

Models of the giant planets are reviewed. The theoretical techniques used in computing the models are described, and the observational and experimental inputs are summarized. Special emphasis is placed on uncertainties in these input data. The models are then examined and the results of various authors presented. It is demonstrated that all the planets have heavy-element enhancements of between 10 and 40 earth masses, with a large fraction of this material residing in the core. It is also shown that the ratio of ice to rock in Uranus and Neptune is on the order of three. The implications of these results for theories of the origin of the solar system are discussed.

Podolak, M.↗
DOE OSTI2023

Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting

Abstract Estimating uncertainty in flood model predictions is important for many applications, including risk assessment and flood forecasting. We focus on uncertainty in physics‐based urban flooding models. We consider the effects of the model's complexity and uncertainty in key input parameters. The effect of rainfall intensity on the uncertainty in water depth predictions is also studied. As a test study, we choose the Interconnected Channel and Pond Routing (ICPR) model of a part of the city of Minneapolis. The uncertainty in the ICPR model's predictions of the floodwater depth is quantified in terms of the ensemble variance using the multilevel Monte Carlo (MC) simulation method. Our results show that uncertainties in the studied domain are highly localized. Model simplifications, such as disregarding the groundwater flow, lead to overly confident predictions, that is, predictions that are both less accurate and uncertain than those of the more complex model. We find that for the same number of uncertain parameters, increasing the model resolution reduces uncertainty in the model predictions (and increases the MC method's computational cost). We employ the multilevel MC method to reduce the cost of estimating uncertainty in a high‐resolution ICPR model. Finally, we use the ensemble estimates of the mean and covariance of the flood depth for real‐time flood depth forecasting using the physics‐informed Gaussian process regression method. We show that even with few measurements, the proposed framework results in a more accurate forecast than that provided by the mean prediction of the ICPR model.

Kohanpur, Amir H.↗
NASA NTRS1974

NASTRAN variance analysis and plotting of HBDY elements

The NASTRAN Thermal Analyzer has been intended to do variance analysis and plot the thermal boundary elements. The objective of the variance analysis addition is to assess the sensitivity of temperature variances resulting from uncertainties inherent in input parameters for heat conduction analysis. The plotting capability provides the ability to check the geometry (location, size and orientation) of the boundary elements of a model in relation to the conduction elements. Variance analysis is the study of uncertainties of the computed results as a function of uncertainties of the input data. To study this problem using NASTRAN, a solution is made for both the expected values of all inputs, plus another solution for each uncertain variable. A variance analysis module subtracts the results to form derivatives, and then can determine the expected deviations of output quantities.

Harder, R. L.↗
NASA NTRS1986

The evolution of very low mass stars

The results of numerical evolutionary calculations for stars with masses in the range of 0.01-0.10 solar mass are presented. The stellar models by which these stars are followed from the early stages of contraction through deuterium burning to the very late stages of degenerate cooling at ages comparable to that of the Galaxy are described, and the methodology used to investigate the major uncertainties in the input physics are discussed. It is found that, for brown dwarfs with masses substantially below the end of the hydrogen-burning main sequence, the evolution of the effective temperature and bolometric luminosity are fairly well determined, despite the residual uncertainties in the input physics. In particular, the evolution is remarkably insensitive to the choice of the atmospheric opacity law at low temperatures and to the amount of mismatch in specific entropy across the stellar envelope. The minimum mass for a star to attain main-sequence status is moderately sensitive to the assumed input physics, particularly the surface boundary conditions.

Nelson, L. A.↗
DOE OSTI2020

A Metallic Fuel Performance Benchmark Problem Based on the IFR-1 Experiment

Metallic nuclear fuels are an active area of research and development for use in advanced reactors. Robust and accurate metallic fuel performance models are necessary for the design, safety analysis, and licensing of such reactors. However, metallic fuel performance models are not as mature as ceramic fuel performance models and require additional development. In order to better gauge the accuracy of existing models, identify high-priority models for development, and quantify the improvements made by future model development, ORNL has developed a benchmark problem based on the IFR-1 experiment. This work gathers all relevant information on the IFR-1 experiment and uses it to develop the benchmark problem. It simulates the problem using the fuel performance code BISON and compare the results to post-irradiation examination data from the experiment. Then a sensitivity analysis is performed on the BISON model to determine the benchmark problem’s sensitivity to uncertainty in the input parameters. The results suggest that BISON’s mechanical models require additional development. Plastic deformation is under-predicted in the cladding and axial swelling is over-predicted in the fuel. These problems may be related. Additionally, there is a bias in the temperature which may be a result of uncertainty in the input parameters rather than an issue with the fuel performance models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

MLUQ (Uncertainty quantification for ML closure models) [SWR-24-36]

Data-based closure models are increasingly being used to replace physic-based closure models because of their flexibility and the growing availability of data. However, closure models are subject to uncertainty because of lack of data in parts of the input space (epistemic uncertainty) or because of noise in the data (aleatoric uncertainty). This software contains a toolbox for training bayesian neural nets that can estimate both uncertainties. A specific treatment is provided to ensure accuracy outside of the data distribution. A set of tools are provided to reduce the dimensionality of the uncertain parameter space, thereby enabling fast uncertainty propagation.

Hassanaly, Malik↗
DOE OSTI2023

Uncertainty-aware photovoltaic generation estimation through fusion of physics with harmonics information using Bayesian neural networks

We develop an aggregate photovoltaic generation estimation methodology that uses diverse inputs and can reason on its current input-dependent predictive uncertainty. Named PVPHEst, for PhotoVoltaic Physics- & Harmonics-driven Estimator, the resulting tool is intelligently weighing and fusing information carried by the output of physics models, harmonics, and line sensors, using Bayesian neural networks and related techniques aimed at solving machine learning problems with intrinsic uncertainty quantification. As each of the three input classes carries heterogeneous information that only sheds light on one facet of the estimation problem but its value can diminish in the face of diverse grid phenomena, PV-PHEst with its estimation and uncertainty reasoning capabilities perform a nontrivial and potentially mission-critical task of value to grid operators.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2016

Probabilistic Modeling Of Ocular Biomechanics In VIIP: Risk Stratification

Visual Impairment and Intracranial Pressure (VIIP) syndrome is a major health concern for long-duration space missions. Currently, it is thought that a cephalad fluid shift in microgravity causes elevated intracranial pressure (ICP) that is transmitted along the optic nerve sheath (ONS). We hypothesize that this in turn leads to alteration and remodeling of connective tissue in the posterior eye which impacts vision. Finite element (FE) analysis is a powerful tool for examining the effects of mechanical loads in complex geometries. Our goal is to build a FE analysis framework to understand the response of the lamina cribrosa and optic nerve head to elevations in ICP in VIIP. To simulate the effects of different pressures on tissues in the posterior eye, we developed a geometric model of the posterior eye and optic nerve sheath and used a Latin hypercubepartial rank correlation coef-ficient (LHSPRCC) approach to assess the influence of uncertainty in our input parameters (i.e. pressures and material properties) on the peak strains within the retina, lamina cribrosa and optic nerve. The LHSPRCC approach was repeated for three relevant ICP ranges, corresponding to upright and supine posture on earth, and microgravity [1]. At each ICP condition we used intraocular pressure (IOP) and mean arterial pressure (MAP) measurements of in-flight astronauts provided by Lifetime Surveillance of Astronaut Health Program, NASA Johnson Space Center. The lamina cribrosa, optic nerve, retinal vessel and retina were modeled as linear-elastic materials, while other tissues were modeled as a Mooney-Rivlin solid (representing ground substance, stiffness parameter c1) with embedded collagen fibers (stiffness parameters c3, c4 and c5). Geometry creationmesh generation was done in Gmsh [2], while FEBio was used for all FE simulations [3]. The LHSPRCC approach resulted in correlation coefficients in the range of 1. To assess the relative influence of the uncertainty in an input parameter on the peak strains, we ranked and then normalized these coefficients, considering that normalized values 0.5 implied a substantial influence on the range of the peak strains in the optic nerve head (ONH). IOP and ICP were found to have a major influence on the peak strains in the ONH, as did optic nerve and LC stiffness. Interestingly, the stiffness of the sclera far from the scleral canal did not have a large influence on peak strains in ONH tissues; however, the collagen fiber stiffness in the peripapillary sclera and annular ring both influenced the peak strains within the ONH. We have created a physiologically relevant model that incorporated collagen fibers to study the effects of elevated ICP. Elevated ICP resulted in strains in the optic nerve that are not predicted to occur on earth: the upright or supine conditions. We found that IOP, ICP, lamina cribrosa stiffness and optic nerve stiffness had the highest association with these extreme strains in the ONH. These extreme strains may activate mechanosensitive cells that induce tissue remodeling and are a risk factor for the development of VIIP.

biomechanics↗
DOE OSTI2022

Sensitivity and uncertainty of the IFR-1 BISON benchmark

The fuel performance code BISON is being used to evaluate metallic fuel for a new fast-spectrum test reactor called the Versatile Test Reactor, which is being considered for adoption by the US Department of Energy. To quantify the accuracy of BISON predictions, researchers at Oak Ridge National Laboratory have been developing a series of benchmarks based on legacy metallic fuel experiments. As part of this effort, the sensitivity of BISON predictions to variations in model inputs and the uncertainties associated with BISON predictions must be established. This paper summarizes efforts to perform a comprehensive sensitivity analysis (SA) and uncertainty quantification (UQ) on a benchmark based on the IFR-1 experiment.For the SA, at least one input was chosen from every BISON model and physics module used in the benchmark. The inputs were varied individually in a series of BISON simulations. Here, the resulting variations in benchmark predictions were normalized to calculate sensitivities. These sensitivities were then used to inform input selections for the UQ.The UQ was performed using the Monte Carlo UQ method. A literature review was conducted to estimate uncertainty distributions for the selected inputs, and values were sampled randomly from each distribution in a series of BISON simulations. Variations in the benchmark predictions were used to estimate uncertainty distributions and confidence intervals. It was found that nearly 100% of the benchmark predictions matched the corresponding legacy values within the confidence intervals. However, this is at least partially because the confidence intervals associated with benchmark predictions were wide. The uncertainty contributions of assumptions in the benchmark, experimental uncertainties, and BISON models were quantified. Some analysis was performed to identify inputs that contributed to the uncertainties. Finally, recommendations are made for future benchmark and future BISON development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Sensitivity and Uncertainty of the IFR-1 BISON Benchmark

The fuel performance code BISON is being used to evaluate metallic fuel for a new fast-spectrum test reactor called the Versatile Test Reactor, which is being considered by the US Department of Energy. To quantify the accuracy of BISON predictions, researchers at Oak Ridge National Laboratory have been developing a series of benchmarks based on legacy metallic fuel experiments. As part of this effort, the sensitivity of BISON predictions to variations in model inputs and the uncertainties associated with BISON predictions must be established. This report summarizes efforts to perform a comprehensive sensitivity analysis (SA) and uncertainty quantification (UQ) on a benchmark based on the IFR-1 experiment. For the SA, at least one input was chosen from every BISON model and physics module used in the benchmark. The inputs were varied individually in a series of BISON simulations. The resulting variations in benchmark predictions were normalized to calculate sensitivities. The strongest sensitivities were identified and used to inform input selections for the UQ. The UQ was performed using the Monte Carlo UQ method. A literature review was conducted to estimate uncertainty distributions for the selected inputs, and values were sampled randomly from each distribution in a series of BISON simulations. Variations in the benchmark predictions were used to estimate uncertainty distributions and confidence intervals. It was found that nearly 100% of benchmark predictions matched the corresponding legacy values within the confidence intervals. However, this is at least partially because of the wide confidence intervals associated with the benchmark predictions. The uncertainty contributions of assumptions in the benchmark, experimental uncertainties, and BISON models were quantified. Some analysis was performed to identify inputs that contributed to the uncertainties. Finally, recommendations are made for future benchmark development and future BISON development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2012

Linear Estimation of Particle Bulk Parameters from Multi-Wavelength Lidar Measurements

An algorithm for linear estimation of aerosol bulk properties such as particle volume, effective radius and complex refractive index from multiwavelength lidar measurements is presented. The approach uses the fact that the total aerosol concentration can well be approximated as a linear combination of aerosol characteristics measured by multiwavelength lidar. Therefore, the aerosol concentration can be estimated from lidar measurements without the need to derive the size distribution, which entails more sophisticated procedures. The definition of the coefficients required for the linear estimates is based on an expansion of the particle size distribution in terms of the measurement kernels. Once the coefficients are established, the approach permits fast retrieval of aerosol bulk properties when compared with the full regularization technique. In addition, the straightforward estimation of bulk properties stabilizes the inversion making it more resistant to noise in the optical data. Numerical tests demonstrate that for data sets containing three aerosol backscattering and two extinction coefficients (so called 3 + 2 ) the uncertainties in the retrieval of particle volume and surface area are below 45% when input data random uncertainties are below 20 %. Moreover, using linear estimates allows reliable retrievals even when the number of input data is reduced. To evaluate the approach, the results obtained using this technique are compared with those based on the previously developed full inversion scheme that relies on the regularization procedure. Both techniques were applied to the data measured by multiwavelength lidar at NASA/GSFC. The results obtained with both methods using the same observations are in good agreement. At the same time, the high speed of the retrieval using linear estimates makes the method preferable for generating aerosol information from extended lidar observations. To demonstrate the efficiency of the method, an extended time series of observations acquired in Turkey in May 2010 was processed using the linear estimates technique permitting, for what we believe to be the first time, temporal-height distributions of particle parameters.

linear estimation↗