Narrow Results

In the complexity modeling, variance decomposition technique is widely used for the quantification of the variation in the output variables explained by covariates. In this work, the satisfaction of sampling-based variance decomposition strategy (SVDS) is firstly testified in the implementation of an analytic method for uncertainty and sensitivity analysis (UASA) of complex systems. Results suggest that SVDS may overvalue the impacts from individual covariates alone but underestimate the effects from their interactions when the model under discussion involves the interaction effects of nonlinear problems of individual covariates. Following the phenomenon, a modification of SVDS is proposed to generate sensitivity measures that well coincide with the analytic method. The testified strategy, together with our proposed modification, is then employed to clarify the roles of infectious rate and recovered rate, as well as of their interaction, in the estimation of equilibrium state (ES) for both SIR and SIS models. Results demonstrate that infectious and recovered rates almost play the same roles less crucial than that acted by the initial susceptible individuals in the decision of ES for SIR model, accompanied by a fragile contribution from their interactions; while in SIS model, infectious rate is more robust than recovered rate, and their interaction effect is also non-ignorable.

Reactor simulation is an important source of uncertainties for a reactor neutrino experiment. Therefore, how to evaluate the antineutrino flux uncertainty results from reactor simulation is an important question. In this study, a method of the antineutrino flux uncertainty result from reactor simulation was proposed by considering the correlation coefficient. In order to use this method in the Daya Bay antineutrino experiment, the open source code DRAGON was improved and used for obtaining the fission fraction and correlation coefficient. The average fission fraction between DRAGON and SCIENCE code was compared and the difference was less than 5% for all the four isotopes. The uncertainty of fission fraction was evaluated by comparing simulation atomic density of four main isotopes with Takahama-3 experiment measurement. After that, the uncertainty of the antineutrino flux results from reactor simulation was evaluated as 0.6% per core for Daya Bay antineutrino experiment.

To improve the predictive ability of computational fluid dynamics (CFD) on the transonic buffet phenomenon, NASA SC(2)-0714 supercritical airfoil is numerically investigated by noninstructive probabilistic collocation method for uncertainty quantification. Distributions of uncertain parameters are established according to the NASA wind tunnel report. The effects of the uncertainties on lift, drag, mean pressure and root-mean square pressure are discussed. To represent the stochastic solution, the mean and standard deviation of variation of flow quantities such as lift and drag coefficients are computed. Furthermore, mean pressure distribution and root-mean square pressure distribution from the upper surface are displayed with uncertainty bounds containing 95% of all possible values. It is shown that the most sensitive part of flow to uncertain parameters is near the shock wave motion region. Comparing uncertainty bounds with experimental data, numerical results are reliable to predict the reduced frequency and mean pressure distribution. However, for root-mean square pressure distribution, numerical results are higher than the experimental data in the trailing edge region.

In order to reveal the operating characteristics of the pumpjet propulsor, standard $k$–$𝜀$, standard $k$–$\omega$, RNG $k$–$𝜀$ and SST $k$–$\omega$ turbulence models were used to conduct steady calculation for the whole flow channels. By comparing the calculation results with experimental data, it was found that the calculation errors were very large in some operating conditions. Therefore, the uncertainty analysis was carried out at all operating conditions of the pumpjet propulsor and the error source was finally determined that it is mainly derived from the model error. Then, the applicability of different turbulence models was analyzed to numerical simulation for the pumpjet propulsor by comparing the internal and external characteristics. It can be seen that the strong turbulent kinetic energy in the guide vane will inevitably cause energy loss, but not necessarily in the impeller. In this area, the increase of turbulent kinetic energy will enhance the mixing and transport of fluids, and the impeller makes the fluids get more energy. In addition, a modified hybrid Reynolds Average Numerical Simulation/Large Eddy Simulation (RANS/LES) model was proposed for unsteady calculation, and the performances, internal flow states and the interaction between the pump and the outer region were further revealed under various conditions of the pumpjet propulsor, which provides some references for predicting accurately and selecting conditions optimally in the future.

This paper considers how the accuracy of range-dependent propagation modeling affects the results of matched-field inversion (MFI) for seabed geoacoustic parameters. In MFI, the forward problem of computing the acoustic fields associated with candidate geoacoustic models is solved a large number of times. Given significant mismatch due to measurement and theory errors, together with the computationally intensive nature of MFI, the appropriate tradeoff between modeling accuracy and computational speed is not obvious. This tradeoff is considered here in terms of the degradation in information content for the geoacoustic parameters that results from inaccurate propagation modeling. The information content is quantified using the marginal posterior probability distributions of the geoacoustic parameters, as computed from a fast Gibbs sampling approach to Bayesian inversion. A synthetic example of this analysis is presented in which the parabolic equation is used to model acoustic fields for a shallow-water, upslope environment, with different levels of modeling accuracy/speed controlled by the range and depth step sizes of the computational grid.

It is urgent to introduce life cycle assessment (LCA) into eco-design in order to conduct eco-design in the quantitative and systematic era. In the design phase, various uncertainties in product life cycle inventory (LCI) are emerged. In practice, the real value of LCI calculated in the product end-of-life phase may be much different from the target LCI value predicted in the design phase. The aim of this research is to propose a robust design method to overcome the uncertainty issue. Regarding the scope of LCI analysis, this paper focused on the product manufacturing phase and its end-of-life phase. In this paper, the design problem of robust eco-design is modeled in a mathematical way, the novel sensitivity coefficient of LCI with uncertainty is proposed to solve the robust eco-design problem from mathematical perspective, and a guideline-based approach for robust design is proposed based on the new sensitivity coefficients. A case study is provided to illustrate the application of this research and validate our methods.

For vehicle-bridge system, structural uncertainties, especially the interval variables with correlation, have a great influence on dynamic response. Therefore, this paper proposes an effective uncertainty analysis method for vehicle-bridge system based on multidimensional parallelepiped (MP) model, which can reasonably deal with the correlation of interval variables. First, the vehicle-bridge system is simplified as a four degrees-of-freedom mass-spring vehicle model running on a simply supported beam. MP model is adopted to describe the uncertainties of all the interval variables. Second, via affine coordinate system transform, the interval variables with correlation are transformed as the independent variables, which is very convenient for uncertainty analysis. Finally, the uncertain dynamic response is approximated through the first-order Taylor interval expansion, and the upper and lower bounds can be calculated using the dynamic response at midpoints and the partial difference multiplied by interval radius. Because the correlation is sufficiently considered, the uncertainty analysis results on vehicle–bridge interaction system will be much more accurate than the traditional interval analysis method (IAM). Numerical example demonstrates the correctness and effectiveness of the proposed method.

A Bayesian methodology to construct probabilistic seismic demand models for the components of a structural system is developed. Existing deterministic models and observational data are used. The demand models are combined with previously developed capacity models for reinforced concrete (RC) bridge columns to estimate the seismic fragilities of bridge components and systems. The approach properly accounts for all relevant uncertainties, including model error. Application to two bridge examples typical of modern California practice is presented.

Fragility functions that estimate the probability of exceeding different levels of damage in slab-column connections of existing non-ductile reinforced concrete buildings subjected to earthquakes are presented. The proposed fragility functions are based on experimental data from 16 investigations conducted in the last 36 years that include a total of 82 specimens. Fragility functions corresponding to four damage states are presented as functions of the level of peak interstory drift imposed on the connection. For damage states involving punching shear failure and loss of vertical carrying capacity, the fragility functions are also a function of the vertical shear in the connection produced by gravity loads normalised by the nominal vertical shear strength in the absence of unbalanced moments. Two sources of uncertainty in the estimation of damage as a function of lateral deformation are studied and discussed. The first is the specimen-to-specimen variability of the drifts associated with a damage state, and the second the epistemic uncertainty arising from using small samples of experimental data and from interpreting the experimental results. For a given peak interstorey drift ratio, the proposed fragility curves permit the estimation of the probability of experiencing different levels of damage in slab-column connections.

Many researches focus on the effect of interfacial friction on Split Hopkinson Pressure Bar (SHPB) tests of concrete materials, in which SHPB is one of the most widely used techniques for determination of the dynamic stress–strain response. However, except for the interfacial friction, the stress–strain response is also affected by many other parameters, such as the errors of specimen dimensions and material parameters. In this paper, the sensitivity of these parameters is comprehensively analyzed through a series of SHPB tests and simulations. The influence of different strengths of cement mortars and different strain rates of cylindrical specimens is considered, the orthogonal experimental design method and nonprobability interval algorithm are used for sensitivity and uncertainty analysis. Research results proved that the interfacial friction is the most sensitive parameter in the present test conditions, and the peak stresses measured by SHPB tests are in good agreement with the interval of uncertainty analysis. This research provides a good reference to understand the uncertain properties of concrete materials under SHPB tests.

By analyzing the influence factors of the investment capacity of power grid, to depreciation cost, sales price and sales quantity, net profit, financing and GDP of the second industry as the dependent variable to build the investment capacity analysis model. After carrying out Kolmogorov-Smirnov test, get the probability distribution of each influence factor. Finally, obtained the grid investment capacity uncertainty of analysis results by Monte Carlo simulation.