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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Uncertainty evaluation for twist drilling stability model

This paper describes the first uncertainty analysis for drilling stability using a frequency-domain drilling stability model. The stability model inputs include: the modal parameters for the torsional-axial vibration mode from the twist drill-holder-spindle axial frequency response function; and the mechanistic coefficients that relate the torque and thrust force to chip area for the selected drill-workpiece material combination. Furthermore, Monte Carlo simulation is applied to propagate the input uncertainties to output uncertainty in the predicted stability map, which separates stable from unstable (chatter) zones in the spindle speed-chip width parameter space. Additionally, the mean stability boundary and its 95% confidence intervals are determined for five cases: varying all four inputs simultaneously and varying them individually. This enables the individual sensitivities to be compared. Experimental results from drilling tests are included for comparison to the prediction. Additionally, Matlab code is provided to implement the stability model and Monte Carlo uncertainty analysis.

42 ENGINEERING↗
DOE OSTI2020

SCALE Code System

The SCALE Code System is a widely used modeling and simulation suite for nuclear safety analysis and design that is developed, maintained, tested, and managed by the Reactor and Nuclear Systems Division (RNSD) of Oak Ridge National Laboratory (ORNL). SCALE provides a comprehensive, verified and validated, user-friendly tool set for criticality safety, reactor and lattice physics, radiation shielding, spent fuel and radioactive source term characterization, and sensitivity and uncertainty analysis. Since 1980, regulators, licensees, and research institutions around the world have used SCALE for safety analysis and design. SCALE provides an integrated framework with dozens of computational modules, including three deterministic and three Monte Carlo radiation transport solvers that are selected based on the desired solution strategy. SCALE includes current nuclear data libraries and problem-dependent processing tools for continuous-energy (CE) and multigroup (MG) neutronics and coupled neutron-gamma calculations, as well as activation, depletion, and decay calculations. SCALE includes unique capabilities for automated variance reduction for shielding calculations, as well as sensitivity and uncertainty analysis. SCALE’s graphical user interfaces assist with accurate system modeling, visualization of nuclear data, and convenient access to desired results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multilevel Monte Carlo Estimators For Derivative-Free Optimization Under Uncertainty

Optimization is a key tool for scientific and engineering applications; however, in the presence of models affected by uncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Optimization under uncertainty (OUU) deals with this endeavor and requires uncertainty quantification analyses at several design locations; i.e., its overall computational cost is proportional to the cost of performing a forward uncertainty analysis at each design location. An OUU workflow has two main components: an inner loop strategy for the computation of statistics of the quantity of interest, and an outer loop optimization strategy tasked with finding the optimal design, given a merit function based on the inner loop statistics. Here, in this work, we propose to alleviate the cost of the inner loop uncertainty analysis by leveraging the so-called multilevel Monte Carlo (MLMC) method, which is able to allocate resources over multiple models with varying accuracy and cost. The resource allocation problem in MLMC is formulated by minimizing the computational cost given a target variance for the estimator. We consider MLMC estimators for statistics usually employed in OUU workflows and solve the corresponding allocation problem. For the outer loop, we consider a derivative-free optimization strategy implemented in the SNOWPAC library; our novel strategy is implemented and released in the Dakota software toolkit. We discuss several numerical test cases to showcase the features and performance of our approach with respect to its Monte Carlo single fidelity counterpart.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Coal Fired Power Plant Configuration and Operation Impact on Plant Effluent Contaminants and Conditions

The primary objective of this project is to characterize coal contaminants in coal-fired power plant wastewater as a function of coal type, unit configurations, and unit operation profile with uncertainty analysis. This project was in response to the U.S. Department of Energy (DOE) Solicitation DE-FOA-0001842. The project duration was between September 01, 2018, and December 31, 2021 (no-cost extension filed, due to the Covid-19 pandemic restrictions, and approved). Field and lab test program was conducted with the main goal to characterize coal contaminants in coal-fired power plant wastewater as a function of coal type, unit configurations, and unit operation profile with uncertainty analysis. In this project, the team of Lehigh University (prime recipient) and Western Kentucky University identified two suitable Thermoelectric Power Plants (TTPs) firing bituminous and sub-bituminous coals respectively, designed test plans, and performed sample collection. Sampling included coal from each TTPs power generation units, Wet Flue Gas Desulfurization (WFGD) slurry material and waste-water samples taken from the outlet of the water treatment tank prior to discharge and other pertinent locations. Coal samples are dried, crushed, and pulverized according to the American Society for Testing and Materials (ASTM) methods. The prepared coal samples are analyzed for normal proximate and ultimate analysis tests in addition to the toxic metals and anions according to ASTM methods. The FGD slurry materials are analyzed for toxic metals and anions according to Electric Power Research Institute (EPRI) or Environmental Protection Agency (EPA) methods, as appropriate. The wastewater samples from the water treatment tank outlet are analyzed for toxic metals and anions according to EPA methods. The effluent species analyzed include mercury, arsenic, selenium, nitrate/nitrite, bromide, and chlorine. This project provided results of effluent conditions as a function of coal type, unit configuration, and unit operation profile, and identified the levels of uncertainty in the effluent results.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US

The ratio according to which the precipitation/snowmelt is divided into surface and subsurface water is of primary importance to watershed scale hydrological modeling. The present study compared four different surface runoff and infiltration partition methods in the Richards-equation-based SWAT model (RSWAT), i.e., daily curve number method (DC-RSWAT), hourly curve number method (HC-RSWAT), Green-Ampt method (GA-RSWAT), and effective infiltration capacity method (EIC-RSWAT). These four versions of RSWAT and a daily-curve-number-based SWAT (DC-SWAT) were used to simulate daily flow rate (from 2001 to 2015) at the outlets of the Tuckahoe Creek Watershed (TCW) and Greensboro Watershed (GW) of the Chesapeake Bay in Maryland. Parameter sensitivity analysis, calibration, and prediction uncertainty analysis were conducted. The results indicate that surface runoff and infiltration partition methods have pronounced impacts on model performance. Comparative analysis showed that DC-RSWAT outperformed other versions of RSWAT for both calibration (from 2001 to2007) and validation (from 2008 to 2015) periods in the two test watersheds. In general, DC-RSWAT achieved more accurate deterministic flow simulation and reliable uncertainty estimation than other versions. As such, daily-curve-number method is recommended for surface runoff and infiltration partition in RSWAT for simulating stream flow.

hydrology, modeling, streamflow↗
DOE OSTI2020

Goldsim Modeling of Vadose Zone Transport for E-Area Naval Reactor Component Disposal Areas: Model Description and Benchmarking

This report documents the development of a GoldSim® model of flow and radionuclide transport to the water table through the Naval Reactor Components Disposal Area (NRCDA) waste disposal sites and underlying vadose zones. The model is designed to be used for Monte Carlo uncertainty analysis in support of the E-Area Performance Assessment (PA). This report describes the model and shows results obtained from benchmarking the model to best-estimate deterministic results obtained using a PORFLOW model of NRCDA vadose zone transport. The PORFLOW model is three-dimensional while the GoldSim model is a simplified one-dimensional treatment. Nevertheless, the GoldSim model was able to accurately reproduce PORFLOW results with some adjustment to the nominal dispersion coefficient and vadose zone flow area used as “tuning” parameters. An example of the results obtained comparing GoldSim and PORFLOW calculation of releases of I-129, Tc-99, C-14 and Ni-59 from waste disposal containers at the 643-26E site is shown in Figure 1 below. For all of the test cases evaluated, GoldSim predicted peak concentrations within 6% of the PORFLOW values and peak times agreed within 8% with the majority of the results in better agreement. The close agreement between the two models provides confidence that GoldSim will give results accurately reflecting the behavior of releases from the NRCDA under off-normal operating conditions for sensitivity and uncertainty analysis

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

A Comprehensive Analysis of Uncertainties in Warm-Rain Parameterizations in Climate Models Based on In Situ Measurements

Abstract Because of the coarse grid size of Earth system models (ESMs), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision–coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 km and time steps on the order of 20–30 min. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semianalytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rainwater tendency simulated by the SCE is roughly linear for time steps smaller than 10 min, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 min. After considering all uncertainties, the grid-mean and time-averaged rainwater tendency based on the parameterization schemes is mostly within a factor of 4 of the local benchmark results simulated by SCE.

Meteorology & Atmospheric Sciences↗
NASA NTRS1977

Monte Carlo analysis of uncertainty propagation in a stratospheric model. 1: Development of a concise stratospheric model

A concise model has been developed to analyze uncertainties in stratospheric perturbations, yet uses a minimum of computer time and is complete enough to represent the results of more complex models. The steady state model applies iteration to achieve coupling between interacting species. The species are determined from diffusion equations with appropriate sources and sinks. Diurnal effects due to chlorine nitrate formation are accounted for by analytic approximation. The model has been used to evaluate steady state perturbations due to injections of chlorine and NO(X).

Rundel, R. D.↗
NASA NTRS1995

Analysis of uncertainty in force balance calibration

In order to reduce errors encountered in the measurement and identification of loads using force balances, a third-order forward polynomial relation between loads and output voltages is proposed. This full third-order model represents an alternative to the second-order model currently in use at NASA Langley Research Center (LaRC) and many other installations worldwide. The new model requires the identification of 84 coefficients (including the 28 used presently) for each of the six outputs. The existing LaRC calibration loading sequence is insufficient for identification of many of these 504 coefficients because critical three-load combinations are absent. Accordingly, a new loading sequence that permits the identification of all 504 coefficients has been developed and is described fully. It is apparent from numerical tests that the new third-order model is clearly superior to second-order models in the presence of small amounts of random measurement noise, assuming that there are indeed higher-order interactions between loads. As the amount of noise increases, however, a third-order model becomes less attractive due to its ability to match the noise itself more faithfully than a second-order model. Numerical results suggest that the transition occurs when the magnitude of random noise becomes of the same order as that of the physical higher-order interactions.

Bursal, Faruk H.↗
DOE OSTI2024

Probabilistic Analysis of Uncertainty in ATRC Flux Profiles

ATRC is a replica of the larger ATR design and is used to conduct research and obtain data such as flux measurements, excess reactivity, and loading requirements before being loaded into ATR. One method for determining the impact an experiment will have at ATR is by looking at the axial flux profile along the fuel rod in the corresponding ATRC experiment; however, flux wand measurements includes large amounts of variation which makes drawing conclusions from the data difficult. This poster describes a definitive method to propagate the uncertainty from ATRC measurements using Python code.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Validation of Linear Covariance Techniques for Mars Entry, Descent, and Landing Guidance and Navigation Performance Analysis

Current Monte Carlo-based uncertainty analysis methods may require significant computational resources to evaluate the performance of a closed-loop guidance, navigation, and control system. An attractive alternative, particularly during the preliminary and conceptual design phase, is to use linear covariance analysis, which can provide the same statistical information as Monte Carlo methods at a fraction of the computational load. Linear covariance has already been demonstrated in various spaceflight regimes, but only recently has it been applied to atmospheric flight. In this study, a 6-degree-of-freedom formulation of both a linear covariance and Monte Carlo analysis tools are utilized for a Mars entry, descent, and landing scenario which capture both atmospheric and powered flight phases to support precision landing. Comparison of the performance results shows close agreement between the linear covariance and traditional Monte Carlo methods when incorporating an assortment of guidance algorithms and processing a variety of inertial and relative sensor measurements onboard the lander's navigation filter.

James W. Williams↗
NASA NTRSDate not supplied

Probabalistic Risk Analysis and Thermal Margin Process for an Inflatable Aeroshell

Uncertainties always exist in atmospheric entry aeroheating environments and the thermal response of thermal protection system (TPS) material. These uncertainties are mitigated in the design by ap-plying margin and factors of safety to the TPS. Entry vehicle TPS is often conservatively over-sized for the heat loads that are experienced along the entry trajectory by stacking worst-case scenarios together. Additionally, the current TPS design and margin process used by NASA offers very little insight into the risk of over-temperature during flight and the reliability of the heat shield performance [1,3]. A probabilistic margin process can be used to calculate the amount of TPS margin necessary to survive a given entry heat load at a specified level of risk [2,3,4]. The vehicle’s initial entry state (entry velocity, flight path angle, and entry mass) determines the expected atmospheric entry environmental conditions and resulting heat load that the entry vehicle will experience. If there is flexibility in the entry state, then this process can be used to select an appropriate combination of entry state parameters and TPS size to target a desired reentry reliability. This probabilistic margin process allows engineers to make informed aeroshell design, entry-trajectory design, and TPS performance risk trades while preventing excessive TPS margin from being applied. The probabilistic TPS margin process has been performed to determine TPS thickness and entry heating constraints given an acceptable risk level for the Low Earth Orbit Flight Experiment of an Inflatable Decelerator (LOFTID) flight project. The process is used in a manner to size the entry heat load for a given flexible TPS (FTPS) thickness so that it meets project reliability standards while allowing the FTPS and the underlying inflatable structure (IS) to be pushed to adequately high temperatures. Since the LOFTID project is an experimental flight demonstration, it is de-sired to drive the FTPS and IS to temperatures that cover a large range of their thermal response models’ applicability. This will allow the thermal response models to be better improved and validated post-flight using LOFTID’s extensive instrumentation embedded within the aeroshell. The presentation demonstrates how uncertainty analysis is carried out using an end-to-end Monte Carlo process where three separate Monte Carlo simulations are run in sequence. The first Monte Carlo simulation operates on the entry trajectory model to generate trajectory parameter dispersions that are fed into the second Monte Carlo simulation. The second Monte Carlo simulation operates on the aerothermodynamics model to generate aeroheating parameter dispersions that are fed into the third Monte Carlo simulation. The third Monte Carlo simulation operates on the FTPS material thermal response model to generate the final FTPS/IS thermal response dispersions. The end-to-end Monte Carlo simulation propagates the uncertainties of each model into the next to quantify the resulting uncertainty of the FTPS/IS thermal response. The fractional contributions of the uncertain parameters in the trajectory, aerothermal, and thermal response models to the variance in the FTPS/IS thermal response is determined as a byproduct of the Monte Carlo analysis. The structural uncertainty of the FTPS thermal response model is evaluated by flight relevant ground testing and model error analysis using test measurements. This probabilistic TPS margin process had never been applied to an entry vehicle and it is one of the LOFTID project’s goals to demonstrate its merits.

Steven A. Tobin↗
DOE OSTI2020

Development of new baseline models for U.S. medium office buildings based on commercial buildings energy consumption survey data

Building energy estimation for the building sector under various scenarios are needed for building energy regulation and policy making. This often starts with representative baselines (either empirical baseline or modeled baseline). Commercial Buildings Energy Consumption Survey (CBECS) data is a widely used empirical baseline for U.S. commercial buildings, but none of the existing baseline model are developed to represent the CBECS data. This paper aims to develop new baseline models for the U.S. medium office buildings, which can produce modeled baselines consistent with the CBECS data. Here, we introduced the methodology to create baseline models and the criteria to evaluate the performance of baseline models. The methodology consists of three phases: (1) identification of model inputs, (2) model calibration, and (3) model validation with uncertainty analysis. The evaluation index is the coefficient of variation of the root-mean-square deviation (CV(RMSD)) of site energy use intensities (EUIs) between the modeled baseline and empirical baseline. Then 30 new baseline models for two vintages (pre- and post-1980) and 15 climate zones were created. The evaluation shows that the CV(RMSD) is lower than 0.05 for the modeled baselines produced by the new baseline models. As a comparison, the CV(RMSD) is higher than 0.1 for the existing modeled baselines generated by DOE Commercial Reference Building Models. Further analysis shows that the new baseline models are able to capture the uncertainties of the representative features of existing buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Extension of SCALE/Sampler’s sensitivity analysis

Nuclear data are a major source of uncertainties in reactor physics calculations. The propagation of nuclear data uncertainties to important system responses is instrumental when determining appropriate safety margins in reactor safety analyses. It is also important to understand the major contributors to the observed uncertainties to make recommendations for further measurements and evaluations and aid in the understanding of the studied system. The SCALE code system allows for nuclear data uncertainty analysis based on the random sampling approach as implemented in SCALE’s Sampler sequence. Sampler was recently extended by a sensitivity analysis in terms of the calculation of two correlation-based sensitivity indices. This analysis allows for the identification of the top contributing nuclear reactions to any analyzed output uncertainty. This paper presents the sensitivity indices, along with their interpretation and limitations. It demonstrates the application in an eigenvalue and decay heat analysis for a boiling water reactor fuel assembly.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗