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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Effect of secondary gas-phase reactions (SGR) in pyrolysis of carbon feedstocks for anisotropic carbon materials production – 2: Effects of SGR on tars produced from varying ranks of coal

Herein, this work explores the extent to which secondary gas-phase reactions (SGR) in pyrolysis can be used to modify coal tar chemistries from coals of different ranks in favor of improved anisotropy formation in their respective pitches. Pyrolysis was performed on four different coals of varying bituminous rank (Utah Sufco, Wyoming PRB Black Thunder, Illinois #6, and West Virginia Flying Eagle), with SGR temperatures ranging from 800 to 900° C and nominal SGR gas-phase residence times from 1 to 2.5 s. The oxygen content, aliphatic content, and molecular weight distributions of the coal tar samples were measured to indicate the changes with increasing levels of SGR, and microscopy was also used to measure changes in anisotropy formation in the resulting pitch samples. Generally, for all coals tested, increased levels of pyrolysis SGR led to decreased oxygen and aliphatic content, increased molecular weight sizes, and improved anisotropy formation. However, it was clear that the extent of these property changes depended on the chemistry and rank of the starting coal feedstock. Despite the relatively high rank of the Illinois #6 coal, its respective pitch samples performed poorly in improving anisotropy formation, due to its high sulfur content. The PRB, Sufco, and Flying Eagle coals performed better in their anisotropy formation than Illinois #6, depending on their respective coal ranks. Statistical analysis (analysis of variance, ANOVA) performed on the sample characterization data also suggests that SGR temperature is consistently the most dominant and significant effect on the resulting coal products.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2016

Biological Sampling Variability Study

There are many sources of variability that exist in the sample collection and analysis process. This paper addresses many, but not all, sources of variability. The main focus of this paper was to better understand and estimate variability due to differences between samplers. Variability between days was also studied, as well as random variability within each sampler. Experiments were performed using multiple surface materials (ceramic and stainless steel), multiple contaminant concentrations (10 spores and 100 spores), and with and without the presence of interfering material. All testing was done with sponge sticks using 10-inch by 10-inch coupons. Bacillus atrophaeus was used as the BA surrogate. Spores were deposited using wet deposition. Grime was coated on the coupons which were planned to include the interfering material (Section 3.3). Samples were prepared and analyzed at PNNL using CDC protocol (Section 3.4) and then cultured and counted. Five samplers were trained so that samples were taken using the same protocol. Each sampler randomly sampled eight coupons each day, four coupons with 10 spores deposited and four coupons with 100 spores deposited. Each day consisted of one material being tested. The clean samples (no interfering materials) were run first, followed by the dirty samples (coated with interfering material). There was a significant difference in recovery efficiency between the coupons with 10 spores deposited (mean of 48.9%) and those with 100 spores deposited (mean of 59.8%). There was no general significant difference between the clean and dirty (containing interfering material) coupons or between the two surface materials; however, there was a significant interaction between concentration amount and presence of interfering material. The recovery efficiency was close to the same for coupons with 10 spores deposited, but for the coupons with 100 spores deposited, the recovery efficiency for the dirty samples was significantly larger (65.9% - dirty vs. 53.6% - clean) (see Figure 4.1). Variance component analysis was used to estimate the amount of variability for each source of variability. There wasn’t much difference in variability for dirty and clean samples, as well as between materials, so these results were pooled together. There was a significant difference in amount of concentration deposited, so results were separated for the 10 spore and 100 spore deposited tests. In each case the within sampler variability was the largest with variances of 426.2 for 10 spores and 173.1 for 100 spores. The within sampler variability constitutes the variability between the four samples of similar material, interfering material, and concentration taken by each sampler. The between sampler variance was estimated to be 0 for 10 spores and 1.2 for 100 spores. The between day variance was estimated to be 42.1 for 10 spores and 78.9 for 100 spores. Standard deviations can be calculated in each case by taking the square root of the variance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predictive Modeling of NOx Emissions from Lean Direct Injection of Hydrogen and Hydrogen/Natural Gas Blends Using Flame Imaging and Machine Learning

This research paper explores the use of machine learning to relate images of flame structure and luminosity to measured NOx emissions. Images of reactions produced by 16 aero-engine derived injectors for a ground-based turbine operated on a range of fuel compositions, air pressure drops, preheat temperatures and adiabatic flame temperatures were captured and postprocessed. The experimental investigations were conducted under atmospheric conditions, capturing CO, NO and NOx emissions data and OH* chemiluminescence images from 27 test conditions. The injector geometry and test conditions were based on a statistically designed test plan. These results were first analyzed using the traditional analysis approach of analysis of variance (ANOVA). The statistically based test plan yielded 432 data points, leading to a correlation for NOx emissions as a function of injector geometry, test conditions and imaging responses, with 70.2% accuracy. As an alternative approach to predicting emissions using imaging diagnostics as well as injector geometry and test conditions, a random forest machine learning algorithm was also applied to the data and was able to achieve an accuracy of 82.6%. This study offers insights into the factors influencing emissions in ground-based turbines while emphasizing the potential of machine learning algorithms in constructing predictive models for complex systems.

08 HYDROGEN↗
DOE OSTI2015

Multileaf Collimator Tracking Improves Dose Delivery for Prostate Cancer Radiation Therapy: Results of the First Clinical Trial

Purpose: To test the hypothesis that multileaf collimator (MLC) tracking improves the consistency between the planned and delivered dose compared with the dose without MLC tracking, in the setting of a prostate cancer volumetric modulated arc therapy trial. Methods and Materials: Multileaf collimator tracking was implemented for 15 patients in a prostate cancer radiation therapy trial; in total, 513 treatment fractions were delivered. During each treatment fraction, the prostate trajectory and treatment MLC positions were collected. These data were used as input for dose reconstruction (multiple isocenter shift method) to calculate the treated dose (with MLC tracking) and the dose that would have been delivered had MLC tracking not been applied (without MLC tracking). The percentage difference from planned for target and normal tissue dose-volume points were calculated. The hypothesis was tested for each dose-volume value via analysis of variance using the F test. Results: Of the 513 fractions delivered, 475 (93%) were suitable for analysis. The mean difference and standard deviation between the planned and treated MLC tracking doses and the planned and without-MLC tracking doses for all 475 fractions were, respectively, PTV D{sub 99%} −0.8% ± 1.1% versus −2.1% ± 2.7%; CTV D{sub 99%} −0.6% ± 0.8% versus −0.6% ± 1.1%; rectum V{sub 65%} 1.6% ± 7.9% versus −1.2% ± 18%; and bladder V{sub 65%} 0.5% ± 4.4% versus −0.0% ± 9.2% (P<.001 for all dose-volume results). Conclusion: This study shows that MLC tracking improves the consistency between the planned and delivered doses compared with the modeled doses without MLC tracking. The implications of this finding are potentially improved patient outcomes, as well as more reliable dose-volume data for radiobiological parameter determination.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

NDE reliability and probability of detection (POD) evolution and paradigm shift

The subject of NDE Reliability and POD has gone through multiple phases since its humble beginning in the late 1960s. This was followed by several programs including the important one nicknamed “Have Cracks – Will Travel” or in short “Have Cracks” by Lockheed Georgia Company for US Air Force during 1974–1978. This and other studies ultimately led to a series of developments in the field of reliability and POD starting from the introduction of fracture mechanics and Damaged Tolerant Design (DTD) to statistical framework by Bernes and Hovey in 1981 for POD estimation to MIL-STD HDBK 1823 (1999) and 1823A (2009). During the last decade, various groups and researchers have further studied the reliability and POD using Model Assisted POD (MAPOD), Simulation Assisted POD (SAPOD), and applying Bayesian Statistics. All and each of these developments had one objective, i.e., improving accuracy of life prediction in components that to a large extent depends on the reliability and capability of NDE methods. Therefore, it is essential to have a reliable detection and sizing of large flaws in components. Currently, POD is used for studying reliability and capability of NDE methods, though POD data offers no absolute truth regarding NDE reliability, i.e., system capability, effects of flaw morphology, and quantifying the human factors. Furthermore, reliability and POD have been reported alike in meaning but POD is not NDE reliability. POD is a subset of the reliability that consists of six phases: 1) samples selection using DOE, 2) NDE equipment setup and calibration, 3) System Measurement Evaluation (SME) including Gage Repeatability and Reproducibility (Gage R and R) and Analysis Of Variance (ANOVA), 4) NDE system capability and electronic and physical saturation, 5) acquiring and fitting data to a model, and data analysis, and 6) POD estimation. This paper provides an overview of all major POD milestones for the last several decades and discuss rationale for using Integrated Computational Materials Engineering (ICME), MAPOD, SAPOD, and Bayesian statistics for studying controllable and non-controllable variables including human factors for estimating POD. Another objective is to list gaps between “hoped for” versus validated or fielded failed hardware.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

ANOVA-HDMR structure of the higher order nodal diffusion solution

Nodal diffusion methods still represent a standard in global reactor calculations, but employ some ad-hoc approximations (such as the quadratic leakage approximation) which limit their accuracy in cases where reference quality solutions are sought. In this work we solve the nodal diffusion equations utilizing the so-called higher-order nodal methods to generate reference quality solutions and to decompose the obtained solutions via a technique known as High Dimensional Model Representation (HDMR). This representation and associated decomposition of the solution provides a new formulation of the transverse leakage term. The HDMR structure is investigated via the technique of Analysis of Variance (ANOVA), which indicates why the existing class of transversely-integrated nodal methods prove to be so successful. Furthermore, the analysis leads to a potential solution method for generating reference quality solutions at a much reduced calculational cost, by applying the ANOVA technique to the full higher order solution. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

TESTING MICROVARIABILITY IN QUASAR DIFFERENTIAL LIGHT CURVES USING SEVERAL FIELD STARS

Microvariability consists of small timescale variations of low amplitude in the photometric light curves of quasars and represents an important tool to investigate their inner core. Detection of quasar microvariations is challenging because of their non-periodicity, as well as the need for high monitoring frequency and a high signal-to-noise ratio. Statistical tests developed for the analysis of quasar differential light curves usually show either low power or low reliability, or both. In this paper we compare two statistical procedures to perform tests on several stars with enhanced power and high reliability. We perform light curve simulations of variable quasars and non-variable stars and analyze them with statistical procedures developed from the F-test and the analysis of variance. The results show a large improvement in the power of both statistical probes and a larger reliability when several stars are included in the analysis. The results from the simulations agree with those obtained from observations of real quasars. The high power and high reliability of the tests discussed in this paper improve the results that can be obtained from short and long timescale variability studies. These techniques are not limited to quasar variability; on the contrary, they can be easily implemented for other sources, such as variable stars. Their applications to future research and to the analysis of large-field photometric monitoring archives could reveal new variable sources.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Analyzing Wildland Fire Smoke Emissions Data Using Compositional Data Techniques

By conservation of mass, the mass of wildland fuel that is pyrolyzed and combusted must equal the mass of smoke emissions, residual char and ash. For a given set of conditions, these amounts are fixed. This places a constraint on smoke emissions data which violates statistical assumptions for many of the methods currently used to analyze these data such as linear regression, analysis of variance, and t-tests. These data are inherently multivariate and non-negative parts of a whole. This paper introduces the field of compositional data analysis to the emissions community and provides examples of appropriate statistical treatment of emissions data. It is shown that modified combustion efficiency should not be used as a predictor variable for other smoke emissions because it is not an independent variable. An alternative method based on compositional linear trends to estimate trace gas composition using CO and CO2 is presented. The data used in this paper resulted from projects the DOD/DOE/EPA Strategic 586 Environmental Research and Development Program projects RC-1648 and 1649. The senior 587 author appreciates the guidance and R scripts provided by Prof. Girty at San Diego State 588 University to estimate linear trends by perturbation. J. P.-A. was supported by the Spanish 589 Ministry of Science, Innovation and Universities under the project CODAMET (RTI2018-590 095518-B-C21, 2019-2021). The data used in this study have been previously published and are 591 available in the original publications. DRW conceived the initial manuscript (70 percent) and 592 performed the bulk of the data analysis. JPA provided statistical guidance and compositional data 593 expertise and contributed 20 percent of the manuscript. TJJ and HJ were extensively involved in 594 the study that provided the data. TJJ provide smoke emissions expertise and HJ provided 595 combustion expertise. The authors declare that they have no conflict of interest. The use of trade 596 or firm names in this publication is for reader information and does not imply endorsement by 597 the U.S. Department of Agriculture of any product or service.

simplex, compositional data analysis, balance, log↗
DOE OSTI2023

Comparison of Error Rate Depending on Operator Expertise and Simulator Complexity

This paper analyzes operator's error rate from experiments, depending on the expertise and simulator complexity. This study uses the Rancor Microworld, a simplified simulator developed by INL, and the Compact Nuclear Simulator (CNS), a less simplified simulator developed by the Korea Atomic Energy Research Institute (KAERI). The error rates were measured from simulation data of a total of 72 participants, and the collected error rate data were analyzed using analysis of variance (ANOVA) test.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Optimal PMU design based on sampling model and sensitivity analysis

The precise measurements of the synchrophasor and frequency from phasor measurement units (PMUs) are widely used in power grid applications. With the improvement of the technique, applications always require stable dynamic performance and higher accuracy for the synchrophasor and frequency measurements, which is challenging for PMU development. To evaluate the contribution of PMU hardware to measurement accuracy, this paper proposes a general-purpose sampling model to analyze the measurement error. In the proposed sampling model, a strict mathematical derivation is derived, where its error is purely determined by the parameters of the PMU hardware. The sensitivity analysis is carried out by three methods, including mathematical analysis, computer simulation, and variance-based sensitivity analysis. Through the sensitivity analysis, this paper establishes the systematic formulation and the inclusion of synchrophasor, frequency, and ROCOF. Experimental results based on the real-world testbench involving distribution-level PMUs match the mathematical analysis conclusion, which verifies the correctness of the general-purpose sampling model. Furthermore, a strategy for the optimal PMU design is proposed, which could guide PMU design in the future.

42 ENGINEERING↗
DOE OSTI2015

Metrological analysis of a virtual flowmeter-based transducer for cryogenic helium

The metrological performance of a virtual flowmeter-based transducer for monitoring helium under cryogenic conditions is assessed. At this aim, an uncertainty model of the transducer, mainly based on a valve model, exploiting finite-element approach, and a virtual flowmeter model, based on the Sereg-Schlumberger method, are presented. The models are validated experimentally on a case study for helium monitoring in cryogenic systems at the European Organization for Nuclear Research (CERN). The impact of uncertainty sources on the transducer metrological performance is assessed by a sensitivity analysis, based on statistical experiment design and analysis of variance. In this way, the uncertainty sources most influencing metrological performance of the transducer are singled out over the input range as a whole, at varying operating and setting conditions. This analysis turns out to be important for CERN cryogenics operation because the metrological design of the transducer is validated, and its components and working conditions with critical specifications for future improvements are identified.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Characterizing changes in soil bacterial community structure in response to short-term warming

High altitude alpine meadows are experiencing considerably greater than average increases in soil surface temperature, potentially as a result of ongoing climate change. The effects of warming on plant productivity and soil edaphic variables have been established previously, but the influence of warming on soil microbial community structure has not been well characterized. Here, the impact of 15 months of soil warming (both + 1 and + 2 degrees C) on bacterial community structure was examined in a field experiment on a Tibetan plateau alpine meadow using bar-coded pyrosequencing. Warming significantly changed (P < 0.05) the structure of the soil bacterial community, but the alpha diversity was not dramatically affected. Changes in the abundance of the Actinobacteria and Alphaproteobacteria were found to contribute the most to differences between ambient (AT) and artificially warmed conditions. A variance partitioning analysis (VPA) showed that warming directly explained 7.15% variation in bacterial community structure, while warming-induced changes in soil edaphic and plant phenotypic properties indirectly accounted for 28.3% and 20.6% of the community variance, respectively. Interestingly, certain taxa showed an inconsistent response to the two warming treatments, for example Deltaproteobacteria showed a decreased relative abundance at + 1 degrees C, but a return to AT control relative abundance at + 2 degrees C. This suggests complex microbial dynamics that could result from conditional dependencies between bacterial taxa.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-F-T-386: Analysis of Three QA Methods for Predicting Dose Deviation Pass Percentage for Lung SBRT VMAT Plans

Purpose: To investigate the significance of using pinpoint ionization chambers (IC) and RadCalc (RC) in determining the quality of lung SBRT VMAT plans with low dose deviation pass percentage (DDPP) as reported by ScandiDos Delta4 (D4). To quantify the relationship between DDPP and point dose deviations determined by IC (ICDD), RadCalc (RCDD), and median dose deviation reported by D4 (D4DD). Methods: Point dose deviations and D4 DDPP were compiled for 45 SBRT VMAT plans. Eighteen patients were treated on Varian Truebeam linear accelerators (linacs); the remaining 27 were treated on Elekta Synergy linacs with Agility collimators. A one-way analysis of variance (ANOVA) was performed to determine if there were any statistically significant differences between D4DD, ICDD, and RCDD. Tukey’s test was used to determine which pair of means was statistically different from each other. Multiple regression analysis was performed to determine if D4DD, ICDD, or RCDD are statistically significant predictors of DDPP. Results: Median DDPP, D4DD, ICDD, and RCDD were 80.5% (47.6%–99.2%), −0.3% (−2.0%–1.6%), 0.2% (−7.5%–6.3%), and 2.9% (−4.0%–19.7%), respectively. The ANOVA showed a statistically significant difference between D4DD, ICDD, and RCDD for a 95% confidence interval (p < 0.001). Tukey’s test revealed a statistically significant difference between two pairs of groups, RCDD-D4DD and RCDD-ICDD (p < 0.001), but no difference between ICDD-D4DD (p = 0.485). Multiple regression analysis revealed that ICDD (p = 0.04) and D4DD (p = 0.03) are statistically significant predictors of DDPP with an adjusted r{sup 2} of 0.115. Conclusion: This study shows ICDD predicts trends in D4 DDPP; however this trend is highly variable as shown by our low r{sup 2}. This work suggests that ICDD can be used as a method to verify DDPP in delivery of lung SBRT VMAT plans. RCDD may not validate low DDPP discovered in D4 QA for small field SBRT treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-252: An Investigation of Gamma Knife Frame Definition Error When Using a Pre-Planning Workflow

Purpose: To determine causal factors related to high frame definition error when treating GK patients using a pre-planning workflow. Methods: 160 cases were retrospectively reviewed. All patients received treatment using a pre-planning workflow whereby stereotactic coordinates are determined from a CT scan acquired after framing using a fiducial box. The planning software automatically detects the fiducials and compares their location to expected values based on the rigid design of the fiducial system. Any difference is reported as mean and maximum frame definition error. The manufacturer recommends these values be less than 1.0 mm and 1.5 mm. In this study, frame definition error was analyzed in comparison with a variety of factors including which neurosurgeon/oncologist/physicist was involved with the procedure, number of post used during framing (3 or 4), type of lesion, and which CT scanner was utilized for acquisition. An analysis of variance (ANOVA) approach was used to statistically evaluate the data and determine causal factors related to instances of high frame definition error. Results: Two factors were identified as significant: number of post (p=0.0003) and CT scanner (p=0.0001). Further analysis showed that one of the four scanners was significantly different than the others. This diagnostic scanner was identified as an older model with localization lasers not tightly calibrated. The average value for maximum frame definition error using this scanner was 1.48 mm (4 posts) and 1.75 mm (3 posts). For the other scanners this value was 1.13 mm (4 posts) and 1.40 mm (3 posts). Conclusion: In utilizing a pre-planning workflow the choice of CT scanner matters. Any scanner utilized for GK should undergo routine QA at a level appropriate for radiation oncology. In terms of 3 vs 4 post, it is hypothesized that three posts provide less stability during CT acquisition. This will be tested in future work.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

An assessment of total depletion uncertainties for a GBC-32 dry storage cask with PLUS7 fuels using Monte Carlo sampling and two-way ANOVA F-test

It is important to accurately evaluate depletion uncertainties that exist due to the limitations of a computer code on accuracy in a nuclear depletion calculation for criticality safety analysis. In this work, the bias and bias uncertainty in the effective neutron multiplication factor (k{sub eff}) for the generic 32 PWR-assembly burnup credit (GBC-32) cask system were assessed using the Monte Carlo uncertainty sampling method for three different burnups and the isotopic biases and uncertainties obtained with the calculated and measured isotopic concentrations. The PLUS7 used nuclear fuel assemblies discharged from the Hanbit Nuclear Power Plant Unit 3 were assumed to be loaded into the GBC-32 cask. In addition, the statistical F-test in the two-way analysis of variance (ANOVA) was suggested to efficiently estimate the statistically converged total depletion uncertainty in k{sub eff}, with a significant reduction of computing efforts in the Monte Carlo criticality calculations, and it was applied to the GBC-32 cask system after a sensitivity analysis was performed to select the important nuclides having significant contributions to the total depletion uncertainty. With the statistical F-tests in the two-way ANOVA, 100 criticality calculations were needed to get a statistically converged total depletion uncertainty for 10 and 30 GWD/MTU, while 120 criticality calculations were needed for 50 GWD/MTU. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

An Integrated Study to Analyze Soil Microbial Community Structure and Metabolic Potential in Two Forest Types

Soil microbial metabolic potential and ecosystem function have received little attention owing to difficulties in methodology. In this study, we selected natural mature forest and natural secondary forest and analyzed the soil microbial community and metabolic potential combing the high-throughput sequencing and GeoChip technologies. Phylogenetic analysis based on 16S rRNA sequencing showed that one known archaeal phylum and 15 known bacterial phyla as well as unclassified phylotypes were presented in these forest soils, and Acidobacteria, Protecobacteria, and Actinobacteria were three of most abundant phyla. The detected microbial functional gene groups were related to different biogeochemical processes, including carbon degradation, carbon fixation, methane metabolism, nitrogen cycling, phosphorus utilization, sulfur cycling, etc. The Shannon index for detected functional gene probes was significantly higher (P<0.05) at natural secondary forest site. The regression analysis showed that a strong positive (P<0.05) correlation was existed between the soil microbial functional gene diversity and phylogenetic diversity. Mantel test showed that soil oxidizable organic carbon, soil total nitrogen and cellulose, glucanase, and amylase activities were significantly linked (P<0.05) to the relative abundance of corresponded functional gene groups. Variance partitioning analysis showed that a total of 81.58% of the variation in community structure was explained by soil chemical factors, soil temperature, and plant diversity. Therefore, the positive link of soil microbial structure and composition to functional activity related to ecosystem functioning was existed, and the natural secondary forest soil may occur the high microbial metabolic potential. Although the results can’t directly reflect the actual microbial populations and functional activities, this study provides insight into the potential activity of the microbial community and associated feedback responses of the terrestrial ecosystem to environmental changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

42 ENGINEERING↗