Simulation and the Monte Carlo Method

Rubinstein, Reuven Y.

doi:10.1002/9781118631980

Cited by 1,432 publications

(591 citation statements)

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“…We focused on GHG emissions which is one of the key impacts of laundry activity (Golsteijn et al 2015;Koehler and Wildbolz 2009;de Koning et al 2010;AISE 2015). A Monte Carlo simulation (Rubinstein and Kroese 2016) was used to quantify the variability in GHG footprints of laundry washing due to variability in (i) detergent format, (ii) detergent dosage (actual), (iii) GHG emissions related to detergent production, (iv) wash temperature, (v) machine energy use within a range of temperature settings and (vi) country electricity grid mixes. In addition, we investigated the GHG emissions' variability in a scenario in which the required electricity for washing machines was produced from solar PV.…”

Section: Responsible Editor: Serenella Salamentioning

confidence: 99%

Quantifying drivers of variability in life cycle greenhouse gas emissions of consumer products—a case study on laundry washing in Europe

Shahmohammadi

Steinmann

Clavreul

et al. 2017

Int J Life Cycle Assess

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Purpose Variability in consumer behaviour can significantly influence the environmental performance of products and their associated impacts and this is typically not quantified in life cycle assessments. The goal of this paper is to demonstrate how consumer behaviour data can be used to understand and quantify the variability in the greenhouse gas emissions from domestic laundry washing across Europe. Methods Data from a pan-European consumer survey of product usage and washing habits was combined with internal company data on product format greenhouse gas (GHG) footprints and in-home measurement of energy consumption of laundry washing as well as literature data to determine the GHG footprint of laundry washing. The variability associated with four laundry detergent product formats and four wash temperature settings in washing machines were quantified on a per wash cycle basis across 23 European countries. The variability in GHG emissions associated with country electricity grid mixes was also taken into account. Monte Carlo methods were used to convert the variability in the input parameters into variability of the life cycle GHG emissions. Rank correlation analysis was used to quantify the importance of the different sources of variability. Results and discussion Both inter-country differences in background electricity mix as well as intra-country variation in consumer behaviour are important for determining the variability in life cycle GHG emissions of laundry detergents. The average GHG emissions related to the laundry washing process in the 23 European countries in 2014 was estimated to be 5 × 10 2 g CO 2 − eq/wash cycle, but varied by a factor of 6.5 between countries. Intra-country variability is between a factor of 3.5 and 5.0 (90% interval). For countries with a mainly fossil-based electricity system, the dominant source of variability in GHG emissions results from consumer choices in the use of washing machines. For countries with a relatively low-carbon electricity mix, variability in life cycle GHG emissions is mainly determined by laundry product-related parameters.Conclusions The combination of rich data sources enabled the quantification of the variability in the life cycle GHG emissions of laundry washing which is driven by a variety of consumer choices, manufacturer choices and infrastructural differences of countries. The improved understanding of the variability needs to be balanced against the cost and challenges of assessing of consumer habits.

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Section: Responsible Editor: Serenella Salamentioning

confidence: 99%

Quantifying drivers of variability in life cycle greenhouse gas emissions of consumer products—a case study on laundry washing in Europe

Shahmohammadi

Steinmann

Clavreul

et al. 2017

Int J Life Cycle Assess

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“…Usually, Monte Carlo methods [17,19] are used to simulate a prescribed random behavior of the network loads. That is, random number generators are used to assign specific probability distributions to certain parameters of the loads, thus reflecting the random variations in the loads' operating conditions.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

Summation of Random Harmonic Currents

Ribeiro

2009

Time‐Varying Waveform Distortions in Power Systems

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This chapter deals with the probabilistic modeling of harmonic currents. After some definitions about single current injection models, the vectorial summation of random harmonic currents characterized by distributions independent of time is considered. The basic methods presented in literature for harmonic current summation modeling are discribed: the convolution method, the joint density method and Monte Carlo simulations. Applications of the methods to different case studies give an idea of the accuracy of the models considered.One aspect of assessing the harmonics tolerated by a power system is the estimation of the statistic figures of harmonics arising from the various time-varying/probabilistic sources. The assessment of harmonics is not exact or uniform, since there will be unpredictable variations in either the nonlinear sources and/or parameters of the system which affect the summation.The combination of a number of harmonic time-varying sources will generally lead to less than the arithmetic sum of the maximum values due to uncertainty of magnitude and phase angle. Hence the resulting summation is extremely difficult to estimate accurately.Before introducing the methods that can be adopted to solve the summation problem, it is necessary to introduce and discuss some basic concepts: random harmonic and interharmonic vectors, the harmonic summation principle and essential nomenclature. Time-Varying Waveform Distortions in Power Systems Edited by Paulo F. Ribeiro

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“…Second, a large number N of statistically independent realisations of the random volume fractions were computed using the Monte Carlo method (Knuth 1981;Rubinstein 1981). In particular, the pseudoinverse method (Devroye 1986) was used to sample the support of the random volume fractions (the unit interval) according to the PDF given in Eq.…”

Section: Numerical Solution Of the Stochastic Multiscale Modelmentioning

confidence: 99%

Stochastic multiscale modelling of cortical bone elasticity based on high-resolution imaging

Sansalone

Gagliardi

Desceliers

et al. 2015

Biomech Model Mechanobiol

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Accurate and reliable assessment of bone quality requires predictive methods which could probe bone microstructure and provide information on bone mechanical properties. Multiscale modelling and simulation represent a fast and powerful way to predict bone mechanical properties based on experimental information on bone microstructure as obtained through X-ray-based methods. However, technical limitations of experimental devices used to inspect bone microstructure may produce blurry data, especially in in vivo conditions. Uncertainties affecting the experimental data (input) may question the reliability of the results predicted by the model (output). Since input data are uncertain, deterministic approaches are limited and new modelling paradigms are required. In this paper, a novel stochastic multiscale model is developed to estimate the elastic properties of bone while taking into account uncertainties on bone composition. Effective elastic properties of cortical bone tissue were computed using a multiscale model based on continuum micromechanics. Volume fractions of bone components (collagen, mineral, and water) were considered as random variables whose probabilistic description was built using the maximum entropy principle. The relevance of this approach was proved by analysing a human bone sample taken from the inferior femoral neck. The sample was imaged using synchrotron radiation micro-computed tomography. 3-D distributions of Haversian porosity and tissue mineral density extracted from these images supplied the experimental information needed to build the stochastic models of the volume fractions. Thus, the stochastic multiscale model provided reliable statistical information (such as mean values and confidence intervals) on bone elastic properties at the tissue scale. Moreover, the existence of a simpler "nominal model", accounting for the main features of the stochastic model, was investigated. It was shown that such a model does exist, and its relevance was discussed.

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Simulation and the Monte Carlo Method

Cited by 1,432 publications

References 0 publications

Quantifying drivers of variability in life cycle greenhouse gas emissions of consumer products—a case study on laundry washing in Europe

Quantifying drivers of variability in life cycle greenhouse gas emissions of consumer products—a case study on laundry washing in Europe

Summation of Random Harmonic Currents

Stochastic multiscale modelling of cortical bone elasticity based on high-resolution imaging

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