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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Multiyear Gridded Data Ensemble of Surface Biogenic Carbon Fluxes for North America: Evaluation and Analysis of Results

Accurate and fine-scale estimates of biogenic carbon fluxes are critical for measuring and monitoring the biosphere's responses and feedback to the climate system. Currently available data products from flux towers and model-intercomparison projects struggle to adequately represent spatiotemporal dynamics of surface biogenic carbon fluxes, and to quantify their uncertainties, which also are crucial to atmospheric inversion systems. To address these gaps, we introduce a new perturbed-parameter model ensemble with the CASA model to estimate surface biogenic carbon fluxes at monthly and 3-hourly scales for North America at ~500-m and 5-km resolutions. We first use the Extended Fourier Amplitude Sensitivity Testing to choose the three most sensitive parameters to be perturbed, maximum light use efficiency (E max ), optimal temperature of photosynthesis (T opt ), and temperature response of respiration (Q 10 ). The initial range for each parameter is broadly sampled for the L1 ensemble, but then we pruned E max with site-level primary productivity to derive an L2 ensemble with narrower uncertainty ranges. Ensembles are strongly correlated with site-level results at both monthly and 3-hourly scales, and the spread across L1/L2 ensemble members encompasses the range of AmeriFlux observations. Monthly variability in the L2 ensemble mean is 85% of the observed variability. The L2 ensemble outperforms diverse data products with the highest Taylor skill scores at diurnal to annual scales. Finally, the ensemble's seasonality agrees well with other models for most biome types and in high and middle latitudes, but inconsistencies are found in subtropical and tropical ecoregions and for annual totals over North America.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads in extreme idling conditions

Abstract. Floating offshore wind turbine (FOWT) systems are subject to complex environmental loads, with significant potential for damage in extreme storm conditions. Design simulations in these conditions are required to assess the survivability of the device with some level of confidence. Aero-hydro-servo-elastic engineering tools can be used with a reasonable balance of accuracy and computational efficiency. The models require many input parameters to describe the air and water conditions, the system properties, and the load calculations. Each of these parameters has some possible range, due to either statistical uncertainty or variations with time. Variation in the input parameters can have important effects on the uncertainty in the resulting loads, but it is not practical to perform detailed assessments of the impact of this uncertainty for every input parameter. This work demonstrates a method to identify the input parameters that have the most impact on the loads to focus further inspection. The process is done specifically for extreme storm load cases defined in the International Electrotechnical Commission design requirements for floating offshore wind turbines. The analysis was performed using the International Energy Agency Wind 15 MW offshore reference wind turbine atop the University of Maine VolturnUS-S reference platform in two US offshore wind regions, the Gulf of Maine and Humboldt Bay. It was found that the direction of incident waves and current, yaw misalignment, and the length of mooring line sections were among the primary sensitivities.

17 WIND ENERGY↗
DOE OSTI2021

Sensitivity Analysis of Particle-In-Cell Modeling Parameters in Settling Bed, Bubbling Fluidized Bed and Circulating Fluidized Bed

The objective of the work presented is to perform a preliminary sensitivity analysis of particle-in-cell (PIC) model parameters when applied to settling bed, bubbling fluidized bed, and circulating fluidized bed simulations. These examples correspond to widely different flow conditions commonly seen in chemical engineering applications. Simulations were performed using the PIC method in the open-source software Multiphase Flow with Interphase eXchanges (MFiX) developed by the National Energy Technology Laboratory (NETL). As part of the non-intrusive uncertainty quantification (UQ) analysis, simulation campaigns were generated using Nodeworks. Sampling locations or settings for PIC model parameters were determined using the Latin Hypercube method. Response surfaces were created using radial basis functions (RBF), and Sobol’ indices were estimated to quantify the influence of model parameters on the quantities of interest (QoI). This study marks a first step towards systematically determining optimal ranges for model parameters used in MFiX-PIC. Based on limited experience, it is expected that these values would depend strongly on flow conditions. Given the complexity of the multiphase flow systems under analysis, a non-intrusive UQ based approach is used to identify the most influential parameters in each case. This prior knowledge will help in proposing an effective design of experiments (DoE) and determine optimal parameters through techniques such as deterministic or Bayesian calibration, which will be pursued in the future.

42 ENGINEERING↗
DOE OSTI2021

Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments

Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Mission-driven design methodology for optical systems

Disclosed are devices, systems and methods for a mission-driven design framework for optical systems. An exemplary method for improving the design of an optical system includes receive a first plurality of parameters indicative of performance requirements of the optical system, receive a second plurality of parameters representative of characteristics of one or more components in the optical system, generate a mathematical representation that combines the first plurality of parameters and the second plurality of parameters, perform a sensitivity analysis to obtain a ranking of parameters from the first plurality of parameters and the second plurality of parameters, select, based on the ranking of the parameters, at least one parameter that produces a largest change in the mathematical representation, and produce a constraint file comprising the at least one parameter for ingestion by a design tool to enable the design of the optical system.

Higbee, Shawn D.↗
DOE OSTI2020

Sensitivity Analysis of Salt Storage Cavern Mechanical Integrity Test Parameters

This report examines the sensitivity of salt cavern Mechanical Integrity Tests (MIT) to uncertainties in key test parameters. MIT's are used by cavern operators to detect and quantify leak rates in access wells in underground salt storage caverns and involve a suite of measured and assumed parameters that have a direct impact on the sensitivity of the testing to detect actual leaks from the cavern storage system. Determining the sensitivity of the testing to these different parameters provides a basis for understanding the results from, and informing the design criteria for, this type of testing. Without fully understanding the sensitivity of the test to the testing parameters, it is possible that the test results may not accurately reflect the integrity of the cavern system; an actual leak may be missed, or an intact system may be interpreted as leaking. This report reviews the main parameters included in MITs and examines how selected changes in their values can impact test results. The deviations used in the sensitivity analyses were designed to be within the ranges believed to be similar to those which may be encountered during testing. The results show that small, plausible fluctuations in some of the parameters measured values can have a significant impact on the testing results. Of the parameters studied here, the sensitivity analyses showed the order of importance to be (from highest to lowest): nitrogen-oil interface depth measurement, well bore temperature, well head pressure, and finally the internal geometry of the testing interval.

42 ENGINEERING↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Assessment of BISON capabilities for component-level prediction of tritium transport in fusion and fission applications

Here we assessed the hydrogen migration and redistribution model implemented in the BISON fuel performance code for component-level tritium transport applications in fusion and fission example scenarios. We developed BISON models of an ITER heat exchanger, light water reactor fuel cladding, and a fluoride salt cooled high temperature reactor heat exchanger. Reasonable agreement between reported values in reference studies and BISON predictions demonstrated the ability of the BISON models to predict tritium transport behavior through steel materials. Next, sensitivity and uncertainty analysis was used to understand key parameter sensitivities within the BISON model for evaluation of tritium migration and redistribution. . The sensitivity analysis showed that the diffusivity activation energy was the most important parameter and that the heat of transport was the least important parameter for tritium migration in steels. Overall, the capabilities of the BISON code for component level modeling of tritium transport are promising.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Uncertainty Quantification and Sensitivity Analysis of Nuclear Construction Cost Reduction Pathways

High construction costs have plagued recent nuclear projects and they hamper the widespread deployment of nuclear technology. The Nuclear Cost Reduction Tool is a plant economic framework that quantifies the impact that various plant design and construction attributes have on construction costs and cost overruns and shows the positive effects of learning over a series of deployments. However, a downside of the current model is that all model outputs and capabilities are deterministic. To provide a more comprehensive view, this study evaluated the impact of model parameter uncertainty through a sensitivity analysis applied to 18 model parameters. This approach quantified the impact of model uncertainty on the output variables of Net Overnight Capital Cost (Net OCC), Construction Duration (CD), and Levelized Cost of Electricity (LCOE). Monte Carlo analysis revealed uncertainty distributions for these variables, showing that absolute uncertainty decreases over a series of deployments. A local sensitivity analysis showed that even small parameter perturbations (5%) can have a significant impact on project execution, highlighting areas that could reduce costs by billions across an order book of plants. The results of this study have improved the understanding of the model and identified the most impactful model parameters and construction attributes.

Levelized Cost of Electricity↗
DOE OSTI2022

Sensitivity and uncertainty analysis of PWR spent fuel observables to operational and model parameters

Sensitivity and uncertainty analyses of spent nuclear fuel (SNF) observables - decay heat, neutron and γ-ray emission rate - to operational and model parameters have been performed. A 2D model representing a typical PWR 17x17 UO{sub 2} fuel assembly has been taken as reference. The Serpent code and ENDF/B-VII.1 evaluated nuclear data library have been used for the analyses. Relative uncertainty of decay heat, neutron and γ-ray emission rates due to Monte Carlo counting statistics, as well as the number of fuel pin radial regions required for an accurate characterization of SNF neutron emission, have been estimated. A parametric study has been carried out to investigate the impact of the homogenization to an average value of the fuel and moderator temperature, boron concentration power and burnup on the prediction of the selected observables. In addition, linear sensitivity coefficients have been calculated. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dynamic Rupture Modeling of the 1999 Chi-Chi, Taiwan Earthquake Using 3DFinite Element Method. Sensitivity Analysis of Slip Rate Function to Model Parameters of Crustal Weak-Zone

The main objective of this study was the dynamic rupture modeling of the M7.3 1999 Chi-Chi, Taiwan earthquake. The purpose of the numerical modeling was two folded. First, using models of rupture dynamics, derive kinematic rupture characteristics, including spatial and temporal variations of the slip rate time history and rupture speed, in the long-period ground motion generation area (LMGA), strong motion generation areas (SMGAs) and background fault area for a thrust shallow rupture. Second, investigate the sensitivity of source time function characteristics in the LMGA and SMGA areas to model parameterization of low-velocity weak zone, such as thickness and shear wave velocity.

58 GEOSCIENCES↗
DOE OSTI2022

A Tanks-in-Series Approach to Estimate Parameters for Lithium-Ion Battery Models

Advanced Battery Management Systems (BMS) play a vital role in monitoring, predicting, and controlling the performance of lithium-ion batteries. BMS employing sophisticated electrochemical models can help increase battery cycle life and minimize charging time. However, in order to realize the full potential of electrochemical model-based BMS, it is critical to ensure accurate predictions and proper model parameterization. The accuracy of the predictions of an electrochemical model is dependent on the accuracy of its parameters, the values of which might change with battery cycling and aging. Parameter estimation for an electrochemical model is generally challenging due to the nonlinear nature and computational complexity of the model equations. To this end, this work utilizes the recently proposed Tanks-in-Series model for Li-ion batteries (J.Electrochem. Soc., 167, 013534 (2020)) to perform parameter estimation. The Tanks-in-Series approach allows for substantially faster parameter estimation compared to the original pseudo two-dimensional (p2D) model. The objective of this work is thus to demonstrate the gain in computational efficiency from the Tanks-in-Series approach. A sensitivity analysis of model parameters is also performed to benchmark the fidelity of the Tanks-in-Series model.

25 ENERGY STORAGE↗
DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Physiological Controls on Carbon Fluxes and Biomass Production in Miscanthus: Insights From a Process‐Based Agroecosystem Model

Biomass crops serve as essential feedstocks for renewable energy and bioproducts and play a critical role in achieving lower emissions in the transportation sector. However, dedicated perennial biomass crops such as Miscanthus × giganteus (Miscanthus) remain underrepresented in process-based agroecosystem models, limiting robust evaluation of their economic and environmental performance. In this study, we developed a data-constrained representation of the sterile triploid Miscanthus (IL clone) within the process-based model ecosys, integrating global sensitivity analysis, ensemble simulation, and parameter calibration. Planting, harvesting, and fertilization practices consistent with field management were incorporated, and phenology was constrained using PhenoCam-derived Green Chromatic Coordinate (GCC) data. Using the Morris global sensitivity analysis method, we identified 11 key physiological parameters governing plant carbon, water, and nutrient relations, particularly processes associated with CO 2 assimilation. We then conducted ensemble simulations by perturbing these parameters and calibrated the model against eddy covariance fluxes and field-measured biomass. Building on the calibrated operating state, parameter-response analyses show that different photosynthetic processes influence productivity in different ways. Protein allocation determines whether productivity increases toward a higher level, whereas electron transport capacity controls additional gains once protein allocation approaches saturation. These findings demonstrate that parameter importance depends on physiological context and on which photosynthetic processes remain limiting. Calibration and validation against observations show that ecosys can reliably reproduce carbon and water fluxes, as well as both above- and belowground biomass, with post-calibration GPP R 2 improving from 0.67 to 0.95 during the calibration period and remaining high during validation (R 2 = 0.95). These results provide a mechanistic foundation for regional simulations and sustainable bioenergy assessments.

ecosys↗