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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

Reliability of terrestrial ecosystem models highly depends on the quantity and quality of thedata that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes areabundant. However, the knowledge of how much data (data length) and which subset of the time seriesdata (data period) should be used to effectively calibrate the model is still lacking. This study uses theAmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-baseddata assimilation technique for various ecosystem types. Parameterization experiments are thus conductedto explore the impact of both data length and data period on the uncertainty reduction of the posteriormodel parameters and the quantification of site and regional carbon dynamics. We find that: the modelis better constrained when it uses two-year data comparing to using one-year data. Further, two-year datais sufficient in calibrating TEM’s carbon dynamics, since using three-year data could only marginallyimprove the model performance at our study sites; the model is better constrained with the data thathave a higher‘‘climate variability’’than that having a lower one. The climate variability is used to measurethe overall possibility of the ecosystem to experience all climatic conditions including drought and extremeair temperatures and radiation; the U.S. regional simulations indicate that the effect of calibration datalength on carbon dynamics is amplified at regional and temporal scales, leading to large discrepanciesamong different parameterization experiments, especially in July and August. Our findings areconditioned on the specific model we used and the calibration sites we selected. The optimal calibrationdata length may not be suitable for other models. However, this study demonstrates that there may exist athreshold for calibration data length and simply using more data would not guarantee a better modelparameterization and prediction. More importantly, climate variability might be an effective indicator ofinformation within the data, which could help data selection for model parameterization. As a result, we believe ourfindings will benefit the ecosystem modeling community in using multiple-year data to improve modelpredictability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2015

Characterization and calibration of a viscoelastic simplified potential energy clock model for inorganic glasses

In this study, to analyze the stresses and strains generated during the solidification of glass-forming materials, stress and volume relaxation must be predicted accurately. Although the modeling attributes required to depict physical aging in organic glassy thermosets strongly resemble the structural relaxation in inorganic glasses, the historical modeling approaches have been distinctly different. To determine whether a common constitutive framework can be applied to both classes of materials, the nonlinear viscoelastic simplified potential energy clock (SPEC) model, developed originally for glassy thermosets, was calibrated for the Schott 8061 inorganic glass and used to analyze a number of tests. A practical methodology for material characterization and model calibration is discussed, and the structural relaxation mechanism is interpreted in the context of SPEC model constitutive equations. SPEC predictions compared to inorganic glass data collected from thermal strain measurements and creep tests demonstrate the ability to achieve engineering accuracy and make the SPEC model feasible for engineering applications involving a much broader class of glassy materials.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Groundwater flow simulation of the Savannah River Site general separations area

The most recent groundwater flow model of the General Separations Area, Savannah River Site, is referred to as the “GSA/PORFLOW” model. GSA/PORFLOW was developed in 2004 by porting an existing General Separations Area groundwater flow model from the FACT code to the PORFLOW code. The preceding “GSA/FACT” model was developed in 1997 using characterization and monitoring data through the mid-1990’s. Both models were manually calibrated to field data. Significantly more field data have been acquired since the 1990’s and model calibration using mathematical optimization software has become routine and recommended practice. The current task involved updating the GSA/PORFLOW model using selected field data current through at least 2015, and use of the PEST code to calibrate the model and quantify parameter uncertainty. This new GSA groundwater flow model is named “GSA2016” in reference to the year in which most development occurred. The GSA2016 model update is intended to address issues raised by the DOE Low-Level Waste (LLW) Disposal Facility Federal Review Group (LFRG) in a 2008 review of the E-Area Performance Assessment, and by the Nuclear Regulatory Commission in reviews of tank closure and Saltstone Disposal Facility Performance Assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

TRITIUM TRANSPORT MODELING AT THE PORE SCALE IN CERAMIC BEEDER MATERIALS USING TMAP8

Fusion reactors depend on the blanket material to breed and release tritium at the same rate or faster than it is consumed by the fusion reaction. Cellular ceramic breeders are dense materials that can maintain a high tritium breeding ratio while promoting tritium release thanks to highly connected pores. Assessing the tritium breeding capabilities of these materials requires a combination of extensive experimental and modeling efforts. In this work, we develop and calibrate a multiphysics model of tritium transport. This novel model accounts for ceramic and pore diffusion, trapping and detrapping, and several surface reactions at the pore surface. We perform a sensitivity analysis and calibrate the model by comparing its predictions against experimental measurements of deuterium absorption. The calibrated model is then used to model tritium absorption in samples with different pore microstructures to investigate the effect of pore interconnectivity on tritium absorption. The model is part of the development of the multiscale, multiphysics framework for tritium transport (i.e., the Tritium Migration Analysis Program [TMAP8]), which is itself built on top of the finite element multiphysics framework MOOSE. This study demonstrates some of TMAP8’s capabilities, and is the first step toward assessing the tritium breeding capabilities of ceramic breeder material designs.

08 HYDROGEN↗
DOE OSTI2022

Tritium Transport Modeling at the Pore Scale in Ceramic Breeder Materials Using TMAP8

Fusion reactors depend on the blanket material to breed and release tritium at the same rate or faster than it is consumed by the fusion reaction. Cellular ceramic breeders (CCBs) are dense materials that can maintain a high tritium breeding ratio while promoting tritium release because of highly connected pores. Assessing the tritium breeding capabilities of these materials requires a combination of extensive experimental and modeling efforts. In this work, we develop and calibrate a multiphysics model of tritium transport. This novel model accounts for ceramic and pore diffusion, trapping and detrapping, and several surface reactions at the pore surface. We perform a sensitivity analysis and calibrate the model by comparing its predictions against experimental measurements of deuterium absorption. The calibrated model is then used to model tritium absorption in samples with different pore microstructures to investigate the effect of pore interconnectivity on tritium absorption. The model is part of the development of the multiscale, multiphysics framework for tritium transport [i.e., the Tritium Migration Analysis Program (TMAP8)], which is itself built on top of the finite-element multiphysics framework multiphysics object-oriented simulation environment (MOOSE). This study demonstrates some of TMAP8’s capabilities and is the first step toward assessing the tritium breeding capabilities of ceramic breeder material designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Test-Based Calibration of Safety Factors for Capacity Models

In this paper, a simple procedure to calibrate the safety factor of capacity models is presented. The calibration can be carried out based on any available database of experimental tests, even of limited size. The procedure aims to assess the model capability of predicting the test results and to calibrate the safety factor so that the capacity equation meets the target reliability level required by the code or sought by the calibrator. After predicting each test of the database with the capacity equation under consideration, the test-prediction pairs are checked for the property of linearity, and the relative error for the properties of homoscedasticity and normality. Once these properties are fulfilled—which may require a nonlinear transformation of the test values and/or the predictions—the closed-form equation proposed in this paper is employed to compute a target design value. The model safety factor is finally obtained by comparing such target design value with the design value obtained from the code. The paper also proposes two approximate analytical equations to compute the tolerance factor, used to attain any given fractile, as a function of the (even small) number of tests, with any assigned confidence level. A fundamental outcome of the procedure is that it yields an objective indicator of the model accuracy, measured by the standard deviation of its error, which may be regarded as a parameter useful for selecting the most reliable model among different competing ones. In the long run, the application of the proposed procedure will allow achieving a uniform reliability level throughout all capacity models used in codes and guidelines. A further advantage is that the partial safety factors so derived can be straightforwardly updated when more experiments become available. Finally, as an example, the proposed procedure is herein applied to the ACI 318 shear design capacity equation for concrete members unreinforced in shear.

42 ENGINEERING↗
DOE OSTI2015

Bayesian Calibration of the Community Land Model using Surrogates

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditioned on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models. We find that accurate surrogate models can be created for CLM in most cases. The posterior distributions lead to better prediction than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters’ distributions significantly. The structural error model reveals a correlation time-scale which can potentially be used to identify physical processes that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

Bayesian calibration↗
DOE OSTI2013

Supercomputer assisted generation of machine learning agents for the calibration of building energy models. In: XSEDE '13 Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery, Article No. 22

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the "Autotune" research which employs machine learning algorithms to generate agents for the different kinds of standard reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.

Sanyal, Jibonananda↗
DOE OSTI2021

Analysis and Integration of the Hydraulic Fracturing Test Site-2 (HFTS-2) Comprehensive Dataset

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Permian (Delaware) Basin. The unique aspect of this program was the acquisition of a unique, comprehensive, diagnostic dataset. Additionally, shorter parent wells drilled three years before the child wells offered clear distinction between the stages influenced by parent-child effects and the stages without any effects. The goal of this study was to analyze and integrate this comprehensive diagnostic dataset to understand the areal and vertical extent of hydraulic fractures (HF). The paper also provides insights on the effects of parent wells’ depletion on child well HF geometry based on various monitoring methods and subsurface models. Areal and vertical coverage for all HFTS-2 wells during stimulation and depletion was estimated based on analysis and interpretation of diagnostics and advanced modeling results. HFTS-2 diagnostics included microseismic (MS), pre- and post-stimulation logs and cores, bottomhole gauges, and fiber optic (FO) data. The diagnostics results (MS, FO, image logs) were integrated and used to calibrate subsurface models. Additional field tests were designed and implemented for depletion monitoring. The tailored program for monitoring depletion included vertical and slant well pressures, interference testing, and a vertical strain depletion trial. Areal Coverage: Conventional MS (and FO MS) were used to compute HF dimensions, which were compared with diagnostics (FO strain, gauge, image logs) observations and calibrated subsurface models. A post-production interference test did not show offset well communication. Vertical Coverage: Vertical coverage during stimulation was monitored using a vertical monitoring well. The stronger mechanical strain signals showed good correlation with MS event intensities, geomechanical properties, and gauge inferences. Vertical depletion was estimated based on vertical/slant well gauges and strain depletion tests. Parent-Child Effects: Diagnostics and calibrated subsurface models show asymmetry in child well fracture geometries for stages that overlap parent wells. Child well image logs serve as a good indicator for parent Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R004/2477423/urtec-2021-5241-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5241 well HF tracking. Child well MS events had an eastward bias, in line with pre-stimulation image logs, and was confirmed by parent well frac hits. Novel/Additive Information: The dataset presents a unique, over-constrained problem space to compare independent techniques to arrive at HF metrics (i.e., stimulation height and/or half-length), unlike a single source dataset, in which calibration is done using available data to guide predictions. Here, the asymmetry in HF geometry seen in the stages influenced by parent-child effects offers unique insights into well spacing and landing, which are key capital decisions the unconventional resources industry is seeking to optimize.

58 GEOSCIENCES↗
DOE OSTI2026

Building Energy Analysis of Manufactured and Multifamily Housing Types in Juneau, Alaska

This report details the results of building energy modeling analysis evaluating the potential energy savings, economic outcomes, and grid-level electricity reduction associated with cold climate air source heat pump (ccASHP) adoption across multifamily and manufactured housing (MMFH) building typologies in the City and Borough of Juneau (CBJ). Three building archetypes were evaluated: multifamily 4-plex apartments, multifamily 8-plex apartments, and manufactured housing units. Building energy models were developed using OpenStudio-HPXML and calibrated to actual utility consumption data and local meteorological data from the Juneau International Airport weather station using an automated calibration tool following the BPI-2400-S-2015 v.2 standard for model calibration. Occupant behaviors present the greatest variability in successful calibrations. Calibrated models were benchmarked against a baseline electric resistance heating condition, with the selected ccASHP modeled as the retrofit condition and typical meteorological year weather data for all results generation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

A constitutive model for sheared dense fiber suspensions

Here we propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

Calibration Designs for Non-Monolithic Wind Tunnel Force Balances

This research paper investigates current experimental designs and regression models for calibrating internal wind tunnel force balances of non-monolithic design. Such calibration methods are necessary for this class of balance because it has an electrical response that is dependent upon the sign of the applied forces and moments. This dependency gives rise to discontinuities in the response surfaces that are not easily modeled using traditional response surface methodologies. An analysis of current recommended calibration models is shown to lead to correlated response model terms. Alternative modeling methods are explored which feature orthogonal or near-orthogonal terms.

Wind tunnel models↗
DOE OSTI2016

SU-F-E-20: A Mathematical Model of Linac Jaw Calibration Integrated with Collimator Walkout

Purpose: Accurate jaw calibration is possible, but it does not necessarily achieve good junctions because of collimator rotation walkout. We developed a mathematical model seeking to pick an origin for calibration that minimizes the collimator walkout effect. Methods: We use radioopaque markers aligned with crosshair on the EPID to determine the collimator walkout at collimator angles 0°, 90° and 270°. We can accurately calibrate jaws to any arbitrary origin near the radiation field centre. While the absolute position of an origin moves with the collimator walkout, its relative location to the crosshair is an invariant. We studied two approaches to select an optimal origin. One approach seeks to bring all three origin locations (0°–90°–270°) as close as possible by minimizing the perimeter of the triangle formed by these points. The other approach focuses on the gap for 0°–90° junctions. Results: Our perimeter cost function has two variables and non-linear behaviour. Generally, it does not have zero-perimeter-length solution which leads to perfect jaw matches. The zero solution can only be achieved, if the collimator rotates about a single fixed axis. In the second approach, we can always get perfect 0°–0° and 0°–90° junctions, because we ignore the 0°–270° situation. For our TrueBeams, both techniques for selecting an origin improved junction dose inhomogeneities to less than ±6%. Conclusion: Our model considers the general jaw matching with collimator rotations and proposes two potential solutions. One solution optimizes the junction gaps by considering all three collimator angles while the other only considers 0°–90°. The first solution will not give perfect matching, but can be clinically acceptable with minimized collimator walkout effect, while the second can have perfect junctions at the expense of the 0°–270° junctions. Different clinics might choose between these two methods basing on their clinical practices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Generator Dynamic Model Validation and Parameter Calibration Using Phasor Measurements at the Point of Connection

Disturbance data recorded by phasor measurement units (PMU) offers opportunities to improve the integrity of dynamic models. However, manually tuning parameters through play-back events demands significant efforts and engineering experiences. In this paper, a calibration method using the extended Kalman filter (EKF) technique is proposed. The formulation of EKF with parameter calibration is discussed. Case studies are presented to demonstrate its validity. The proposed calibration method is cost-effective, complementary to traditional equipment testing for improving dynamic model quality.

Huang, Zhenyu↗
DOE OSTI2017

Parameter Calibration of GTN Damage Model and Formability Analysis of 22MnB5 in Hot Forming Process

Hot forming of high strength steel at elevated temperatures is an attractive technology to achieve the lightweight of vehicle body. The mechanical behavior of boron steel 22MnB5 strongly depends on the variation of temperature which makes the process design more difficult. In this paper, the Gurson–Tvergaard–Needleman (GTN) model is used to study the formability of 22MnB5 sheet at different temperatures. Firstly, the rheological behavior of 22MnB5 is analyzed through a series of hot tensile tests at a temperature range of 600-800 °C. Then, a detailed process to calibrate the damage parameters is given based on the response surface methodology and genetic algorithm method. The GTN model together with the damage parameters calibrated is then implemented to simulate the deformation and damage evolution of 22MnB5 in the process of high-temperature Nakazima test. The capability of the GTN model as a suitable tool to evaluate the sheet formability is confirmed by comparing experimental and calculated results. Finally, as a practical application, the forming limit diagram of 22MnB5 at 700 °C is constructed using the Nakazima simulation and Marciniak–Kuczynski (M–K) model, respectively. And the simulation integrated GTN model shows a higher reliability by comparing the predicted results of these two approaches with the experimental ones.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Physiologically-based pharmacokinetic model for Fentanyl in support of the development of Provisional Advisory Levels

Provisional Advisory Levels (PALs) are tiered exposure limits for toxic chemicals in air and drinking water that are developed to assist in emergency responses. Physiologically-based pharmacokinetic (PBPK) modeling can support this process by enabling extrapolations across doses, and exposure routes, thereby addressing gaps in the available toxicity data. Here, we describe the development of a PBPK model for Fentanyl – a synthetic opioid used clinically for pain management – to support the establishment of PALs. Starting from an existing model for intravenous Fentanyl, we first optimized distribution and clearance parameters using several additional IV datasets. We then calibrated the model using pharmacokinetic data for various formulations, and determined the absorbed fraction, F, and time taken for the absorbed amount to reach 90% of its final value, t90. For aerosolized pulmonary Fentanyl, F = 1 and t90 < 1 min indicating complete and rapid absorption. The F value ranged from 0.35 to 0.74 for oral and various transmucosal routes. Oral Fentanyl was absorbed the slowest (t90 ∼ 300 min); the absorption of intranasal Fentanyl was relatively rapid (t90 ∼ 20–40 min); and the various oral transmucosal routes had intermediate absorption rates (t90 ∼ 160–300 min). Based on these results, for inhalation exposures, we assumed that all of the Fentanyl inhaled from the air during each breath directly, and instantaneously enters the arterial circulation. We present model predictions of Fentanyl blood concentrations in oral and inhalation scenarios relevant for PAL development, and provide an analytical expression that can be used to extrapolate between oral and inhalation routes for the derivation of PALs. - Highlights: • We develop a Fentanyl PBPK model for relating external dose to internal levels. • We calibrate the model to oral and inhalation exposures using > 50 human datasets. • Model predictions are in good agreement with the available pharmacokinetic data. • The model can be used for extrapolating across routes, doses and exposure durations. • We illustrate how the model can be used for developing Provisional Advisory Levels.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Bayesian calibration of the Community Land Model using surrogates

We present results from the Bayesian calibration of hydrological parameters of the Community Land Model (CLM), which is often used in climate simulations and Earth system models. A statistical inverse problem is formulated for three hydrological parameters, conditional on observations of latent heat surface fluxes over 48 months. Our calibration method uses polynomial and Gaussian process surrogates of the CLM, and solves the parameter estimation problem using a Markov chain Monte Carlo sampler. Posterior probability densities for the parameters are developed for two sites with different soil and vegetation covers. Our method also allows us to examine the structural error in CLM under two error models. We find that surrogate models can be created for CLM in most cases. The posterior distributions are more predictive than the default parameter values in CLM. Climatologically averaging the observations does not modify the parameters' distributions significantly. The structural error model reveals a correlation time-scale which can be used to identify the physical process that could be contributing to it. While the calibrated CLM has a higher predictive skill, the calibration is under-dispersive.

54 ENVIRONMENTAL SCIENCES↗