• Nuclear Engineering and Technology
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• v.37 no.3
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• pp.245-258
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• 2005

This paper addresses two types of uncertainty: stochastic uncertainty and subjective uncertainty in probabilistic accident consequence assessments. The off-site consequence assessment code OSCAAR has been applied to uncertainty and sensitivity analyses on the individual risks of early fatality and latent cancer fatality in the population outside the plant boundary due to a severe accident. A new stratified meteorological sampling scheme was successfully implemented into the trajectory model for atmospheric dispersion and the statistical variability of the probability distributions of the consequence was examined. A total of 65 uncertain input parameters was considered and 128 runs of OSCAAR with 144 meteorological sequences were performed in the parameter uncertainty analysis. The study provided the range of uncertainty for the expected values of individual risks of early and latent cancer fatality close to the site. In the sensitivity analyses, the correlation/regression measures were useful for identifying those input parameters whose uncertainty makes an important contribution to the overall uncertainty for the consequence. This could provide valuable insights into areas for further research aiming at reducing the uncertainties.

• International journal of steel structures
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• v.18 no.5
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• pp.1607-1616
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• 2018

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4296-4309
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• 2022

Mesh and turbulence model sensitivity analyses have been performed on computational fluid dynamics simulations executed with Hydra and ANSYS Fluent for a single CANadian Deuterium Uranium (CANDU) 37M nuclear fuel bundle placed within a standard pressure tube. The goal of this work was to perform a methodical analysis to objectively determine an appropriate mesh and to gauge the sensitivity of different turbulence models for CANDU subchannel flow under isothermal conditions. The boundary conditions and material properties are representative of normal operating conditions in a high-powered channel of the Darlington Nuclear Generating Station. Four meshes were generated with ANSYS Workbench Meshing, ranging from 22 to 84 million cells, and analyzed here to determine an appropriate level of mesh resolution and quality. Five turbulence models were compared in the turbulence model sensitivity analysis: standard k - ε, RNG k - ε, realizable k - ε, SST k - ω, and the Reynolds Stress Model. The intent of this work was to gain confidence in mesh generation and turbulence model selection of a single bundle to inform the decision making of subsequent investigations of an entire fuel channel containing a string of twelve bundles.

• Journal of Gynecologic Oncology
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• v.29 no.6
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• pp.98.1-98.13
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• 2018

Objective: We describe a systematic review and meta-analysis of the performance of ${18}F$-fluorodeoxyglucose ($^{18}F-FDG$) positron emission tomography/computed tomography (PET/CT) for detecting metastasis in ovarian cancer. Methods: MEDLINE and Embase were searched for diagnostic accuracy studies that used $^{18}F-FDG$ PET or PET/CT for pre-treatment staging, using surgical findings as the reference standard. Sensitivities and specificities were pooled and plotted in a hierarchic summary receiver operating characteristic plot. Potential causes of heterogeneity were explored through sensitivity analyses. Results: Eight studies with 594 patients were included. The overall pooled sensitivity and specificity for metastasis were 0.72 (95% confidence interval [CI]=0.61-0.81) and 0.93 (95% CI=0.85-0.97), respectively. There was considerable heterogeneity in sensitivity ($I^2=97.57%$) and specificity ($I^2=96.74%$). In sensitivity analyses, studies that used laparotomy as the reference standard showed significantly higher sensitivity and specificity (0.77; 95% CI=0.67-0.87 and 0.96; 95% CI=0.92-0.99, respectively) than those including diagnostic laparoscopy (0.62; 95% CI=0.46-0.77 and 0.84; 95% CI=0.69-0.99, respectively). Higher specificity was shown in studies that confirmed surgical findings by pathologic evaluation (0.95; 95% CI=0.90-0.99) than in a study without pathologic confirmation (0.69; 95% CI=0.24-1.00). Studies with a lower prevalence of the FDG-avid subtype showed higher specificity (0.97; 95% CI=0.94-1.00) than those with a greater prevalence (0.89; 95% CI=0.80-0.97). Conclusion: Pre-treatment $^{18}F-FDG$ PET/CT shows moderate sensitivity and high specificity for detecting metastasis in ovarian cancer. With its low false-positive rate, it can help select surgical approaches or alternative treatment options.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.375-382
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• 1999

Linear-by-linear association models are commonly used to analyze ordered categorical data. To fit these models appropriate scores need to be chosen. In this paper we perform sensitivity analyses in two-way contingency tables to investigate the effect of scores on goodness-of-fits and on tests of significance. In addition we show that the best score which yields the best fit of data can be selected based on the sensitivity analysis results.

• International Journal of Concrete Structures and Materials
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• v.6 no.2
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• pp.101-110
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• 2012

This paper focuses on the finite element (FE) response sensitivity and reliability analyses considering smooth constitutive material models. A reinforced concrete frame is modeled for FE sensitivity analysis followed by direct differentiation method under both static and dynamic load cases. Later, the reliability analysis is performed to predict the seismic behavior of the frame. Displacement sensitivity discontinuities are observed along the pseudo-time axis using non-smooth concrete and reinforcing steel model under quasi-static loading. However, the smooth materials show continuity in response sensitivity at elastic to plastic transition points. The normalized sensitivity results are also used to measure the relative importance of the material parameters on the structural responses. In FE reliability analysis, the influence of smoothness behavior of reinforcing steel is carefully noticed. More efficient and reasonable reliability estimation can be achieved by using smooth material model compare with bilinear material constitutive model.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.398-401
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• 2001

There are two methods to calculate design sensitivity such as direct differentiation method and adjoint method. A sort of direct differentiation method for design sensitivity analysis costs too much when number of design variables is much larger than the number of response functions whose design sensitivity analyses are required. Therefore, an adjoint method is suggested for the case that the dimension of design variables is lager than the number of response function. An adjoint method is required to compute adjoint variables from the simultaneous linear system equation, the so-called adjoint equation, requiring only the eigenvalue and its associated eigenvectors for mode being differentiated. This method has been extended to the repeated eigenvalue problem. In this paper, we propose an adjoint method for deign sensitivity analysis of damped vibratory systems with distinct eigenvalues.

• Structural Engineering and Mechanics
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• v.38 no.1
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• pp.125-140
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• 2011

This study presents an innovative method to estimate the reliability sensitivity based on the low-discrepancy sampling which is a new technique for structural reliability analysis. Two advantages are contributed to the method: one is that, by developing a general importance sampling procedure for reliability sensitivity analysis, the partial derivative of the failure probability with respect to the distribution parameter can be directly obtained with typically insignificant additional computations on the basis of structural reliability analysis; and the other is that, by combining various low-discrepancy sequences with the above importance sampling procedure, the proposed method is far more efficient than that based on the classical Monte Carlo method in estimating reliability sensitivity, especially for problems of small failure probability or problems that require a large number of costly finite element analyses. Examples involving both numerical and structural problems illustrate the application and effectiveness of the method developed, which indicate that the proposed method can provide accurate and computationally efficient estimates of reliability sensitivity.

• Asian Journal of Atmospheric Environment
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• v.12 no.2
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• pp.139-152
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• 2018

The urban model inter-comparison study (UMICS) was conducted in order to improve the performance of air quality models (AQMs) for simulating fine particulate matter ($PM_{2.5}$) in the Greater Tokyo Area of Japan. UMICS consists of three phases: the first phase focusing on elemental carbon (UMICS1), the second phase focusing on sulfate, nitrate and ammonium (UMICS2), and the third phase focusing on organic aerosol (OA) (UMICS 3). In UMICS2/3, all the participating AQMs were the Community Multiscale Air Quality modeling system (CMAQ) with different configurations, and they similarly overestimated $PM_{2.5}$ nitrate concentration and underestimated $PM_{2.5}$ OA concentration. Various sensitivity analyses on CMAQ configurations, emissions and boundary concentrations, and meteorological fields were conducted in order to seek pathways for improvement of $PM_{2.5}$ simulation. The sensitivity analyses revealed that $PM_{2.5}$ nitrate concentration was highly sensitive to emissions of ammonia ($NH_3$) and dry deposition of nitric acid ($HNO_3$) and $NH_3$, and $PM_{2.5}$ OA concentration was highly sensitive to emissions of condensable organic compounds (COC). It was found that $PM_{2.5}$ simulation was substantially improved by using modified monthly profile of $NH_3$ emissions, larger dry deposition velocities of $HNO_3$ and $NH_3$, and additionally estimated COC emissions. Moreover, variability in $PM_{2.5}$ simulation was estimated from the results of all the sensitivity analyses. The variabilities on CMAQ configurations, chemical inputs (emissions and boundary concentrations), and meteorological fields were 6.1-6.5, 9.7-10.9, and 10.3-12.3%, respectively.

• Steel and Composite Structures
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• v.15 no.1
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• pp.15-39
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• 2013

The sensitivities of a structural response due to variation of its design parameters are prerequisite in the majority of the algorithms used for fundamental problems in engineering as system uncertainties, identification and probabilistic assessments etc. The paper presents the concept of probabilistic sensitivity of suspension bridges with respect to near-fault ground motion. In near field earthquake ground motions, large amplitude spectral accelerations can occur at long periods where many suspension bridges have significant structural response modes. Two different types of suspension bridges, which are Bosporus and Humber bridges, are selected to investigate the near-fault ground motion effects on suspension bridges random response sensitivity analysis. The modulus of elasticity is selected as random design variable. Strong ground motion records of Kocaeli, Northridge and Erzincan earthquakes are selected for the analyses. The stochastic sensitivity displacements and internal forces are determined by using the stochastic sensitivity finite element method and Monte Carlo simulation method. The stochastic sensitivity displacements and responses obtained from the two different suspension bridges subjected to these near-fault strong-ground motions are compared with each other. It is seen from the results that near-fault ground motions have different impacts stochastic sensitivity responses of suspension bridges. The stochastic sensitivity information provides a deeper insight into the structural design and it can be used as a basis for decision-making.