Search NASA⌕ Search

SEARCH · Search NASA

Results for “Sensitivity analysis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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

At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

Breast cancer is the most prominent type of cancer among women. Understanding the microenvironment of breast cancer and the interactions between cells and cytokines will lead to better treatment approaches for patients. In this study, we developed a data-driven mathematical model to investigate the dynamics of key cells and cytokines involved in breast cancer development. We used gene expression profiles of tumors to estimate the relative abundance of each immune cell and group patients based on their immune patterns. Dynamical results show the complex interplay between cells and molecules, and sensitivity analysis emphasizes the direct effects of macrophages and adipocytes on cancer cell growth. In addition, we observed the dual effect of IFN-γ on cancer proliferation, either through direct inhibition of cancer cells or by increasing the cytotoxicity of CD8+ T-cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Acoustic Tomography of the Atmosphere: A Large-Eddy Simulation Sensitivity Study

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ( RMSE s) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design.

17 WIND ENERGY↗
DOE OSTI2017

Life Cycle Assessment to support the quantification of the environmental impacts of an event

In recent years, several tools have been used to define and quantify the environmental impacts associated with an event; however, a lack of uniform approaches for conducting environmental evaluations has been revealed. The aim of this paper is to evaluate whether the Life Cycle Assessment methodology, which is rarely applied to an event, can be an appropriate tool for calculating the environmental impacts associated with the assembly, disassembly, and use phase of an event analysing in particular the components and the displays used to establish the exhibits. The aim is also to include the issues reported by ISO 20121:2012 involving the interested parties that can be monitored but also affected by the event owner, namely the event organiser, the workforce and the supply chain. A small event held in Northern Italy was selected as the subject of the research. The results obtained show that the main contributors are energy consumption for lighting and heating and the use of aluminium materials, such as bars for supporting the spotlights, carpet and the electronic equipment. A sensitivity analysis for estimating the effects of the impact assessment method chosen has also been conducted and an uncertainty analysis has been performed using the Monte Carlo technique. This study highlighted the importance of the energy consumed by heating and lighting on the environmental implications, and indicated that the preparation and assembly should always be considered when quantifying the environmental profile of an event. - Highlights: • LCA methodology, developed for products and services, is applied to an event. • A small event held in Northern Italy is analysed. • The main contributors are energy consumption and the use of aluminium and carpet. • Exhibition site preparation can have important environmental implications. • This study demonstrates the importance of the assembly, disassembly and use phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Time-frequency energy density precipitation method for time-of-flight extraction of narrowband Lamb wave detection signals

The time-of-flight of the Lamb wave provides an important basis for defect evaluation in metal plates and is the input signal for Lamb wave tomographic imaging. However, the time-of-flight can be difficult to acquire because of the Lamb wave dispersion characteristics. This work proposes a time-frequency energy density precipitation method to accurately extract the time-of-flight of narrowband Lamb wave detection signals in metal plates. In the proposed method, a discrete short-time Fourier transform is performed on the narrowband Lamb wave detection signals to obtain the corresponding discrete time-frequency energy density distribution. The energy density values at the center frequency for all discrete time points are then calculated by linear interpolation. Next, the time-domain energy density curve focused on that center frequency is precipitated by least squares fitting of the calculated energy density values. Finally, the peak times of the energy density curve obtained relative to the initial pulse signal are extracted as the time-of-flight for the narrowband Lamb wave detection signals. An experimental platform is established for time-of-flight extraction of narrowband Lamb wave detection signals, and sensitivity analysis of the proposed time-frequency energy density precipitation method is performed in terms of propagation distance, dispersion characteristics, center frequency, and plate thickness. For comparison, the widely used Hilbert–Huang transform method is also implemented for time-of-flight extraction. The results show that the time-frequency energy density precipitation method can accurately extract the time-of-flight with relative error of <1% and thus can act as a universal time-of-flight extraction method for narrowband Lamb wave detection signals.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Rainfall erosivity in Slovenia: Sensitivity estimation and trend detection

Highlights: • Rainfall erosivity for 10 rainfall stations located in Slovenia was evaluated. • Rainfall erosivity trends are mostly positive while mostly statistically insignificant. • The maximum rainfall erosivity is obtained under Alpine climate. • Monthly rainfall erosivity was computed. • Sensitivity analyses were carried out. Slovenia is one of the EU countries with the largest values and largest amounts of variability in rainfall erosivity, with maximum annual values exceeding 10,000 MJ mm ha{sup -1} h{sup -1} yr{sup -1}. Five-minute rainfall data was analysed from 10 Slovenian rainfall stations with data-length availability longer than 25 years with a maximum data length of 69 years and a total data-station length equal to 443 years. Trends in the rainfall erosivity R-factor were detected for four different sub-samples using monthly, half-year, and annual rainfall erosivity values. The results indicate that rainfall erosivity trends for the selected Slovenian stations are mostly statistically insignificant, with the selected significance level of 0.05. However, a larger share of identified trends are positive than negative. The maximum annual rainfall erosivity values were obtained for one specific mountain station. Furthermore, a sensitivity analysis regarding the rainfall erosivity factor R calculation showed that the rainfall threshold parameter (12.7 mm) that is used to remove the small-magnitude rainfall events in order to reduce the computational burden can attribute up to 10% of the average annual R-values in cases where this threshold is not used. Other parameters have, on average, a smaller impact on the calculated rainfall erosivity. Furthermore, the application of local kinetic energy equations resulted in, on average, about 20% higher annual rainfall erosivity values compared to the equation that is proposed by the Revised Universal Soil Loss Equation (RUSLE) manual and was not developed specifically for this region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Environmental performance of household waste management in Europe - An example of 7 countries

Highlights: • Attributional LCA of household waste management in 7 EU countries. • Country-specific technologies and waste composition are crucial for impacts. • Proper waste management is a benefit to society. • Waste hierarchy focus needs to change from quantity to quality. • Need for overall improvement in waste data. - Abstract: An attributional life cycle assessment (LCA) of the management of 1 ton of household waste was conducted in accordance with ISO 14044:2006 and the ILCD Handbook for seven European countries, namely Germany, Denmark, France, UK, Italy, Poland and Greece, representing different household waste compositions, waste management practices, technologies, and energy systems. National data were collected from a range of sources regarding household waste composition, household sorting efficiency, collection, waste treatments, recycling, electricity and heat composition, and technological efficiencies. The objective was to quantify the environmental performance in the different countries, in order to analyze the sources of the main environmental impacts and national differences which affect the results. In most of the seven countries, household waste management provides environmental benefits when considering the benefits of recycling of materials and recovering and utilization of energy. Environmental benefits come from paper recycling and, to a lesser extent, the recycling of metals and glass. Waste-to-energy plants can lead to an environmental load (as in France) or a saving (Germany and Denmark), depending mainly on the composition of the energy being substituted. Sensitivity analysis and a data quality assessment identified a range of critical parameters, suggesting from where better data should be obtained. The study concluded that household waste management is environmentally the best in European countries with a minimum reliance on landfilling, also induced by the implementation of the Waste Hierarchy, though environmental performance does not correlate clearly with the rate of material recycling. From an environmental point of view, this calls for a change in the waste management paradigm, with less focus on where the waste is routed and more of a focus on the quality and utilization of recovered materials and energy.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Evolution of US DOE Performance Assessments Over 20 Years - 13597

Performance assessments (PAs) have been used for many years for the analysis of post-closure hazards associated with a radioactive waste disposal facility and to provide a reasonable expectation of the ability of the site and facility design to meet objectives for the protection of members of the public and the environment. The use of PA to support decision-making for LLW disposal facilities has been mandated in United States Department of Energy (US DOE) directives governing radioactive waste management since 1988 (currently DOE Order 435.1, Radioactive Waste Management). Prior to that time, PAs were also used in a less formal role. Over the past 20+ years, the US DOE approach to conduct, review and apply PAs has evolved into an efficient, rigorous and mature process that includes specific requirements for continuous improvement and independent reviews. The PA process has evolved through refinement of a graded and iterative approach designed to help focus efforts on those aspects of the problem expected to have the greatest influence on the decision being made. Many of the evolutionary changes to the PA process are linked to the refinement of the PA maintenance concept that has proven to be an important element of US DOE PA requirements in the context of supporting decision-making for safe disposal of LLW. The PA maintenance concept is central to the evolution of the graded and iterative philosophy and has helped to drive the evolution of PAs from a deterministic compliance calculation into a systematic approach that helps to focus on critical aspects of the disposal system in a manner designed to provide a more informed basis for decision-making throughout the life of a disposal facility (e.g., monitoring, research and testing, waste acceptance criteria, design improvements, data collection, model refinements). A significant evolution in PA modeling has been associated with improved use of uncertainty and sensitivity analysis techniques to support efficient implementation of the graded and iterative approach. Rather than attempt to exactly predict the migration of radionuclides in a disposal unit, the best PAs have evolved into tools that provide a range of results to guide decision-makers in planning the most efficient, cost effective, and safe disposal of radionuclides. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Computational assessment of smooth and rough parameter dependence of statistics in chaotic dynamical systems

An assumption of smooth response to small parameter changes, of statistics or long-time averages of a chaotic system, is generally made in the field of sensitivity analysis, and the parametric derivatives of statistical quantities are critically used in science and engineering. In this paper, we propose a numerical procedure to assess the differentiability of statistics with respect to parameters in chaotic systems. Here, we numerically show that the existence of the derivative depends on the Lebesgue integrability of a certain density gradient function, which we define as the derivative of logarithmic SRB density along the unstable manifold. We develop a recursive formula for the density gradient that can be efficiently computed along trajectories, and demonstrate its use in determining the differentiability of statistics. Our numerical procedure is illustrated on low-dimensional chaotic systems whose statistics exhibit both smooth and rough regions in parameter space.

42 ENGINEERING↗
DOE OSTI2015

Inventory Uncertainty Quantification using TENDL Covariance Data in Fispact-II

The new inventory code Fispact-II provides predictions of inventory, radiological quantities and their uncertainties using nuclear data covariance information. Central to the method is a novel fast pathways search algorithm using directed graphs. The pathways output provides (1) an aid to identifying important reactions, (2) fast estimates of uncertainties, (3) reduced models that retain important nuclides and reactions for use in the code's Monte Carlo sensitivity analysis module. Described are the methods that are being implemented for improving uncertainty predictions, quantification and propagation using the covariance data that the recent nuclear data libraries contain. In the TENDL library, above the upper energy of the resolved resonance range, a Monte Carlo method in which the covariance data come from uncertainties of the nuclear model calculations is used. The nuclear data files are read directly by FISPACT-II without any further intermediate processing. Variance and covariance data are processed and used by FISPACT-II to compute uncertainties in collapsed cross sections, and these are in turn used to predict uncertainties in inventories and all derived radiological data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

High-yield hydrogen production from biomass by in vitro metabolic engineering: Mixed sugars coutilization and kinetic modeling

The use of hydrogen (H 2 ) as a fuel offers enhanced energy conversion efficiency and tremendous potential to decrease greenhouse gas emissions, but producing it in a distributed, carbon-neutral, low-cost manner requires new technologies. Herein we demonstrate the complete conversion of glucose and xylose from plant biomass to H 2 and CO 2 based on an in vitro synthetic enzymatic pathway. Glucose and xylose were simultaneously converted to H 2 with a yield of two H 2 per carbon, the maximum possible yield. Parameters of a nonlinear kinetic model were fitted with experimental data using a genetic algorithm, and a global sensitivity analysis was used to identify the enzymes that have the greatest impact on reaction rate and yield. After optimizing enzyme loadings using this model, volumetric H 2 productivity was increased 3-fold to 32 mmol H 2 ∙L ₋1 ∙h ₋1 . The productivity was further enhanced to 54 mmol H 2 ∙L ₋1 ∙h ₋1 by increasing reaction temperature, substrate, and enzyme concentrations—an increase of 67-fold compared with the initial studies using this method. The production of hydrogen from locally produced biomass is a promising means to achieve global green energy production.

08 HYDROGEN↗
DOE OSTI2021

COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Interestingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the effective reproduction number. Furthermore, the model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously provide information for policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-524: In-Vivo Diode Dosimetry Proton Therapy Range Verification Validation Study for Pediatric CSI

Purpose: With the aim of reducing acute esophageal radiation toxicity in pediatric patients receiving craniospinal irradiation (CSI), we investigated the implementation of an in-vivo, adaptive proton therapy range verification methodology. Simulation experiments and in-phantom measurements were conducted to validate the range verification technique for this clinical application. Methods: A silicon diode array system has been developed and experimentally tested in phantom for passively scattered proton beam range verification for a prostate treatment case by correlating properties of the detector signal to the water equivalent path length (WEPL). We propose to extend the methodology to verify range distal to the vertebral body for pediatric CSI cases by placing this small volume dosimeter in the esophagus of the anesthetized patient immediately prior to treatment. A set of calibration measurements was performed to establish a time signal to WEPL fit for a “scout” beam in a solid water phantom. Measurements are compared against Monte Carlo simulation in GEANT4 using the Tool for Particle Simulation (TOPAS). Results: Measurements with the diode array in a spread out Bragg peak of 14 cm modulation width and 15 cm range (177 MeV passively scattered beam) in solid water were successfully validated against proton fluence rate simulations in TOPAS. The resulting calibration curve allows for a sensitivity analysis of detector system response with dose rate in simulation and with individual diode position through simulation on patient CT data. Conclusion: Feasibility has been shown for the application of this range verification methodology to pediatric CSI. An in-vivo measurement to determine the WEPL to the inner surface of the esophagus will allow for personalized adjustment of the treatment plan to ensure sparing of the esophagus while confirming target coverage. A Toltz acknowledges partial support by the CREATE Medical Physics Research Training Network grant of the Natural Sciences and Engineering Research Council (Grant number: 432290)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many simulations for acceptable accuracy. Surrogate models provide a means of circumventing the high computational expense of sampling such complex models. In particular, polynomial chaos expansions (PCEs) have been successfully used for uncertainty quantification studies of deterministic models where the dominant source of uncertainty is parametric. We discuss an extension to conventional PCE surrogate modeling to enable surrogate construction for stochastic computational models that have intrinsic noise in addition to parametric uncertainty. We develop a PCE surrogate on a joint space of intrinsic and parametric uncertainty, enabled by Rosenblatt transformations, which are evaluated via kernel density estimation of the associated conditional cumulative distributions. Furthermore, we extend the construction to random field data via the Karhunen–Loève expansion. We then take advantage of closed-form solutions for computing PCE Sobol indices to perform a global sensitivity analysis of the model which quantifies the intrinsic noise contribution to the overall model output variance. Additionally, the resulting joint PCE is generative in the sense that it allows generating random realizations at any input parameter setting that are statistically approximately equivalent to realizations from the underlying stochastic model. The method is demonstrated on a chemical catalysis example model and a synthetic example controlled by a parameter that enables a switch from unimodal to bimodal response distributions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Trajectory-Driven Algorithm for Differentiating SRB Measures on Unstable Manifolds

Sinai-Ruelle-Bowen (SRB) measures are limiting stationary distributions describing the statistical behavior of chaotic dynamical systems. Directional derivatives of SRB measure densities conditioned on unstable manifolds are critical in the sensitivity analysis of hyperbolic chaos. These derivatives, known as the SRB density gradients, are by-products of the regularization of Lebesgue integrals appearing in the original linear response expression. In this paper, we propose a novel trajectory- driven algorithm for computing the SRB density gradient defined for systems with high-dimensional unstable manifolds. We apply the concept of measure preservation together with the chain rule on smooth manifolds. Due to the recursive one-step nature of our derivations, the proposed procedure is memory-efficient and can be naturally integrated with existing Monte Carlo schemes widely used in computational chaotic dynamics. Here, we numerically show the exponential convergence of our scheme, analyze the computational cost, and present its use in the context of Monte Carlo integration.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Assessment of SFR Wire Wrap Simulation Uncertainties

Predictive modeling and simulation of nuclear reactor performance and fuel are challenging due to the large number of coupled physical phenomena that must be addressed. Models that will be used for design or operational decisions must be analyzed for uncertainty to ascertain impacts to safety or performance. Rigorous, structured uncertainty analyses are performed by characterizing the model’s input uncertainties and then propagating the uncertainties through the model to estimate output uncertainty. This project is part of the ongoing effort to assess modeling uncertainty in Nek5000 simulations of flow configurations relevant to the advanced reactor applications of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. Three geometries are under investigation in these preliminary assessments: a 3-D pipe, a 3-D 7-pin bundle, and a single pin from the Thermal-Hydraulic Out-of-Reactor Safety (THORS) facility. Initial efforts have focused on gaining an understanding of Nek5000 modeling options and integrating Nek5000 with Dakota. These tasks are being accomplished by demonstrating the use of Dakota to assess parametric uncertainties in a simple pipe flow problem. This problem is used to optimize performance of the uncertainty quantification strategy and to estimate computational requirements for assessments of complex geometries. A sensitivity analysis to three turbulent models was conducted for a turbulent flow in a single wire wrapped pin (THOR) geometry. Section 2 briefly describes the software tools used in this study and provides appropriate references. Section 3 presents the coupling interface between Dakota and a computational fluid dynamic (CFD) code (Nek5000 or STARCCM+), with details on the workflow, the scripts used for setting up the run, and the scripts used for post-processing the output files. In Section 4, the meshing methods used to generate the THORS and 7-pin bundle meshes are explained. Sections 5, 6 and 7 present numerical results for the 3-D pipe, the single pin THORS mesh, and the 7-pin bundle mesh, respectively.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

CFD simulation of MSW combustion and SNCR in a commercial incinerator

Highlights: • Presented a CFD scheme for modeling MSW incinerator including SNCR process. • Performed a sensitivity analysis of SNCR operating conditions. • Non-uniform distributions of gas velocity, temperature and NO{sub x} in the incinerator. • The injection position of reagent was critical for a desirable performance of SNCR. • A NSR 1.5 was recommended as a compromise of NO{sub x} reduction rates and NH{sub 3} slip. - Abstract: A CFD scheme was presented for modeling municipal solid waste (MSW) combustion in a moving-grate incinerator, including the in-bed burning of solid wastes, the out-of-bed burnout of gaseous volatiles, and the selective non-catalytic reduction (SNCR) process between urea (CO(NH{sub 2}){sub 2}) and NO{sub x}. The in-bed calculations provided 2-D profiles of the gas–solid temperatures and the gas species concentrations along the bed length, which were then used as inlet conditions for the out-of-bed computations. The over-bed simulations provided the profiles of incident radiation heat flux on the top of bed. A 3-dimensional benchmark simulation was conducted with a 750 t/day commercial incinerator using the present coupling scheme incorporating with a reduced SNCR reduction mechanism. Numerical tests were performed to investigate the effects of operating parameters such as injection position, injection speed and the normalized stoichiometric ratio (NSR) on the SNCR performance. The simulation results showed that the distributions of gas velocity, temperature and NO{sub x} concentration were highly non-uniform, which made the injection position one of the most sensitive operating parameters influencing the SNCR performance of moving grate incinerators. The simulation results also showed that multi-layer injections were needed to meet the EU2000 standard, and a NSR 1.5 was suggested as a compromise of a satisfactory NO{sub x} reduction and reasonable NH{sub 3} slip rates. This work provided useful guides to the design and operation of SNCR process in moving-grate incinerators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Uncertainty quantification for competing failure mechanisms in unidirectionally reinforced carbon–carbon composites

Microstructure-informed finite element models play a key role in the carbon–carbon composite design process. Variability in manufacturing process parameters and experimental limitations introduce model parameter uncertainty. This study quantifies the effect of model parameter uncertainty on transverse tensile fracture behavior and proposes a methodology to predict the failure mode based on competing microscale damage mechanisms. Finite element simulations incorporate fiber–matrix interface debonding with cohesive zones and matrix damage with a smeared crack band approach in a unidirectional carbon–carbon composite. Results from a variance-based global sensitivity analysis identifies interfacial and matrix damage parameters as the primary source of variability in fracture behavior. Sobol’ indices indicate that matrix and cohesive zone strengths contribute 94% of the variance in the effective ultimate stress. A local analysis elucidates the relationship between these constituent strength parameters and failure mode by estimating the probability of cohesive, matrix, and mixed-mode dominated failure. Based on the results for 4000 simulations, 93% exhibit mixed-mode or interfacial dominated failure, which underscores the crucial role of fiber–matrix interface debonding in the transverse tensile failure of carbon–carbon composites. These uncertainty quantification results facilitate more efficient model calibration and provide a framework for microstructure-informed failure predictions in the face of manufacturing-induced uncertainty.

36 MATERIALS SCIENCE↗