Quantitative deterministic versus probability analyses based on a safety margin criterion

Chen, Zuyu; Li-hong, Chen; Xu, Jiacheng; Sun, Peng; Wu, Chao; Wang, Yujie

doi:10.1007/s11431-014-5638-6

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…Admissible factor of safety for the slope stability of a rockfill dam and resistance to sliding of a gravity dam have been verified by Chen et al [27][28][29]. The results indicate that, under different target operation conditions and methods, the admissible value of factor of safety is quite different, but the RSM always follows ≈ .…”

Section: Basic Principlesmentioning

confidence: 84%

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Safety Criteria and Standards for Bearing Capacity of Foundation

Fan

Chen

et al. 2017

Mathematical Problems in Engineering

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This paper focuses on the evaluation standards of factor of safety for foundation stability analysis. The problem of foundation stability is analyzed via the methods of risk analysis of engineering structures and reliability-based design, and the factor of safety for foundation stability is determined by using bearing capacity safety-factor method (BSFM) and strength safety-factor method (SSFM). Based on a typical example, the admissible factors of safety were calibrated with a target reliability index specified in relevant standards. Two safety criteria and their standards of bearing capacity of foundation for these two methods (BSFM and SSFM) were established. The universality of the safety criteria and their standards for foundation reliability was verified based on the concept of the ratio of safety margin (RSM).

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Section: Basic Principlesmentioning

confidence: 84%

“…Chen et al [27][28][29] studied the criteria for analyzing the resistance to sliding of a rockfill dam and a gravity dam. Chen et al proposed the concept of a RSM and described the relationship between the factor of safety and reliability index.…”

Section: Basic Principlesmentioning

confidence: 99%

Safety Criteria and Standards for Bearing Capacity of Foundation

Fan

Chen

et al. 2017

Mathematical Problems in Engineering

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“…A non-exhaustive list includes: (i) automatic elimination of negligible species and reactions to generate skeletal mechanisms [7][8][9][10][11][12][13]; (ii) dimension reduction techniques, which further decrease the number of species or degrees of freedom that need be considered, including chemical lumping [14,15], mathematical lumping [16], QSSA [17,18], RCCE [19], ILDM [20], REDIM [21] and ICE-PIC [22]; and (iii) storage/retrieval algorithms to reduce the computational cost of repetitive kinetics calculations, including ISAT [23,24] and PRISM [25]. (One component of the current methodology is DRGEP [9], which is in category (i).)…”

Section: Introductionmentioning

confidence: 99%

A pre-partitioned adaptive chemistry methodology for the efficient implementation of combustion chemistry in particle PDF methods

Liang

Pope

Pepiot

2015

Combustion and Flame

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a b s t r a c t Large Eddy Simulation/particle Probability Density Function (LES/PDF) approaches are now well developed, and can be applied to turbulent combustion problems involving complex flows with strong turbulence-chemistry interactions. However, these methods are computationally expensive, restricting their use to simple fuels with relatively small detailed chemical mechanisms. To mitigate the cost in both CPU time and storage requirements, an adaptive strategy tailored for particle PDF methods is presented here, which provides for each particle a specialized reduced representation and kinetic model adjusted to its changing composition. Rather than performing chemical reduction at runtime to determine the optimal set of equations to use for a given particle, an analysis of the composition space likely to be accessed during the combustion simulation is performed in a pre-processing stage using simple Partially Stirred Reactor (PaSR) computations. In the pre-processing stage, the composition space is partitioned into a user-specified number of regions, over which suitable reduced chemical representations and kinetic models are generated automatically using the Directed Relation Graph with Error Propagation (DRGEP) reduction technique. A computational particle in the combustion simulation then carries only the variables present in the reduced representation and evolves according to the reduced kinetic model corresponding to the composition space region the particle belongs to. This region is identified efficiently using a low-dimensional binary-tree search algorithm, thereby keeping the run-time overhead associated with the adaptive strategy to a minimum. The performance of the algorithm is characterized for propane/air combustion in a PaSR with pairwise mixing. The results show that the reduction errors are well controlled by the specified error tolerance, and that the adaptive framework provides significant gains in cost and storage compared to traditional non-adaptive reduction approaches.

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“…Time-scale analysis identifies fast species as well as reactions, and describes their time evolutions by algebraic equations. Methods of time-scale analysis are primarily based on quasi-steady-state (QSS) and partial equilibrium (PE) assumptions [42][43][44][45][46]. Several approaches to identifying the 3 QSS species have been proposed [45][46][47][48][49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Skeletal reaction model generation, uncertainty quantification and minimization: Combustion of butane

Xin

Sheen

Wang

et al. 2014

Combustion and Flame

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Skeletal reaction models for n-butane and iso-butane combustion are derived from a detailed chemistry model through directed relation graph (DRG) and DRG-aided sensitivity analysis (DRGASA) methods. It is shown that the accuracy of the reduced models can be improved by optimization through the method of uncertainty minimization by polynomial chaos expansion (MUM-PCE). The dependence of model uncertainty on the model size is also investigated by exploring skeletal models containing different number of species. It is shown that the dependence of model uncertainty is subject to the completeness of the model. In principle, for a specific simulation the uncertainty of a complete model, which includes all reactions important to its prediction, is convergent with respect to the model size, while the uncertainty calculated with an incomplete model may display unpredictable correlation with the model size.

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Quantitative deterministic versus probability analyses based on a safety margin criterion

Cited by 19 publications

References 29 publications

Safety Criteria and Standards for Bearing Capacity of Foundation

Safety Criteria and Standards for Bearing Capacity of Foundation

A pre-partitioned adaptive chemistry methodology for the efficient implementation of combustion chemistry in particle PDF methods

Skeletal reaction model generation, uncertainty quantification and minimization: Combustion of butane

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