Uncertainty in effective elastic properties of particle filled polymers by the Monte-Carlo simulation

Kamiński, Marcin; Lauke, Bernd

doi:10.1016/j.compstruct.2014.12.063

Cited by 21 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Afterwards, an influence of the input data uncertainty on identification accuracy has been investigated by performing Monte Carlo simulations [46,47]. Gaussian distribution of the input data has been taken into account by considering mean value μ , and two different cases of standard deviation s 1 and s 2 as indicated in Table 4.…”

Section: Resultsmentioning

confidence: 99%

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

Ogierman

2018

Materials

View full text Add to dashboard Cite

This paper is devoted to determination of elastic properties of composite constituents by using an inverse identification procedure. The aim of the developed identification procedure is to compute the elastic constants of individual material phases on the basis of known properties of composite materials. The inverse problem of identification has been solved by combining an evolutionary algorithm with a micromechanical model. The paper also focuses on selection of a suitable micromechanical model for optimization which should ensure a compromise between accuracy and complexity. Two different cases have been studied: composite reinforced with short cylindrical fibers and composite reinforced with cubic particles. Moreover, Monte Carlo simulations have been carried out to expose a difference in outcome of identification which may occur when uncertain input data is considered. Obtained results show that identification is successful only when properties of composite materials with at least two different volume fractions of the reinforcement are known.

show abstract

Section: Resultsmentioning

confidence: 99%

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

Ogierman

2018

Materials

View full text Add to dashboard Cite

show abstract

“…With the development in computer science, the Monte Carlo method proposed in the 1940s has become a popular statistical simulation technology concerned with the numerical computation based on the probability theory [ 60 ]. Recently, this method has been successfully employed to assess the uncertainty of many researches, such as the survey of loading factors of wood products for Chinese residences, the calculation of mass transfer parameters for volatile organic compounds, the study of elastic properties for particles filled polymers, and the analysis of stress variations for composite single lap joints [ 39 , 61 , 62 ]. In a typical run of the Monte Carlo method, the uncertainty of a measurement can be evaluated as follows [ 16 ]:…”

Section: Resultsmentioning

confidence: 99%

An Investigation on Formaldehyde Emission Characteristics of Wood Building Materials in Chinese Standard Tests: Product Emission Levels, Measurement Uncertainties, and Data Correlations between Various Tests

et al. 2015

View full text Add to dashboard Cite

As a large producer and consumer of wood building materials, China suffers product formaldehyde emissions (PFE) but lacks systematic investigations and basic data on Chinese standard emission tests (CST), so this paper presented a first effort on this issue. The PFE of fiberboards, particleboards, blockboards, floorings, and parquets manufactured in Beijing region were characterized by the perforator extraction method (PE), 9–11 L and 40 L desiccator methods (D9, D40), and environmental chamber method (EC) of the Chinese national standard GB 18580; based on statistics of PFE data, measurement uncertainties in CST were evaluated by the Monte Carlo method; moreover, PFE data correlations between tests were established. Results showed: (1) Different tests may give slightly different evaluations on product quality. In PE and D9 tests, blockboards and parquets reached E1 grade for PFE, which can be directly used in indoor environment; but in D40 and EC tests, floorings and parquets achieved E1. (2) In multiple tests, PFE data characterized by PE, D9, and D40 complied with Gaussian distributions, while those characterized by EC followed log-normal distributions. Uncertainties in CST were overall low, with uncertainties for 20 material-method combinations all below 7.5%, and the average uncertainty for each method under 3.5%, thus being acceptable in engineering application. A more complicated material structure and a larger test scale caused higher uncertainties. (3) Conventional linear models applied to correlating PFE values between PE, D9, and EC, with R2 all over 0.840, while novel logarithmic (exponential) models can work better for correlations involving D40, with R2 all beyond 0.901. This research preliminarily demonstrated the effectiveness of CST, where results for D40 presented greater similarities to EC—the currently most reliable test for PFE, thus highlighting the potential of Chinese D40 as a more practical approach in production control and risk assessment.

show abstract

“…Pearson correlation analysis found a strong correlation between the quantity of gravel and the coefficient of uniformity, as shown in Figure 24. Additionally, due to the early stage of the research, as subsequent experimental samples become more abundant, the uncertainty of the data sample characteristic parameters should also be considered [50,51].…”

Section: Parameter Sensitivity and Correlation Analysismentioning

confidence: 99%

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Xiao,

Zhang,

Yan

et al. 2024

Processes

View full text Add to dashboard Cite

To obtain the macroscale Young’s modulus of glutenite under gravel inclusions, a numerical simulation of macroscale Young’s modulus prediction based on the mesoscale characteristics of glutenite was carried out. Firstly, the micron indentation test was used to obtain the meso-mechanical parameters of gravel and matrix in glutenite to ensure the reasonableness of the numerical simulation parameter settings; secondly, a two-dimensional glutenite physical model generation method based on the secondary development of Python was put forward; and then, the macroscale Young’s modulus variation rule of glutenite under different gravel sizes, particle size ratios, and content characteristics were analyzed using the finite element method (FEM). The results show that Young’s modulus of gravel is larger than Young’s modulus of the matrix, and Young’s modulus of different gravel and matrix has some differences. The gravel content is the main controlling factor affecting the macroscale Young’s modulus of glutenite; the gravel content and Young’s modulus of glutenite show a strong positive correlation, and the gravel size and particle size ratio have less influence on the macroscale Young’s modulus of glutenite. The difference in Young’s modulus between gravel and matrix causes the formation of local stress concentrations during loading and compression of glutenite. The smaller the gravel grain size, the higher the degree of non-uniformity of the grain size, the more likely it is to form higher local stresses. The results of the study provide a new prediction method for the prediction of the macroscale Young’s modulus of a glutenite reservoir.

show abstract

Uncertainty in effective elastic properties of particle filled polymers by the Monte-Carlo simulation

Cited by 21 publications

References 18 publications

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

Inverse Identification of Elastic Properties of Constituents of Discontinuously Reinforced Composites

An Investigation on Formaldehyde Emission Characteristics of Wood Building Materials in Chinese Standard Tests: Product Emission Levels, Measurement Uncertainties, and Data Correlations between Various Tests

Simulation Study on the Prediction of Macroscale Young’s Modulus Based on the Mesoscale Characteristics of Tight Glutenite Reservoirs

Contact Info

Product

Resources

About