A Stochastic Analysis of a Solar Heated and Cooled House

1981 ◽  
Vol 103 (2) ◽  
pp. 158-166 ◽  
Author(s):  
Wiwut Tanthapanichakoon ◽  
D. M. Himmelblau
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Analysis ◽  
Probability Distributions ◽  
Random Variables ◽  
Frequency Distributions ◽  
Simulation Techniques ◽  
Standard Deviations ◽  
Model Equations ◽  
Stochastic Responses

Monte Carlo simulation techniques have been used to characterize the stochastic responses of the components of a solar heated and cooled house. Random variables with specified ensemble means, standard deviations, and probability distributions were introduced as inputs and parameters into the model equations for the house, and the equations solved repeatedly to provide samples of the component outputs. The character of the frequency distributions of the outputs, their means and standard deviations, and time statistics are discussed as well as implications with respect to the design of similar systems.

The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models

2010 ◽  
Vol 31 (4) ◽  
pp. 650-661 ◽  
Author(s):  
Bas Groot Koerkamp ◽  
Theo Stijnen ◽  
Milton C. Weinstein ◽  
M. G. Myriam Hunink
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Parameter Uncertainty ◽  
Probability Distributions ◽  
Decision Models ◽  
Medical Decision ◽  
Combined Analysis ◽  
Frequency Distributions ◽  
Stochastic Uncertainty ◽  
Patient Heterogeneity

The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.

scholarly journals Technical-Economic Analysis of Grapple Saw

2020 ◽  
Vol 41 (2) ◽  
pp. 219-229 ◽  
Author(s):  
Ricardo Hideaki Miyajima ◽  
Paulo Torres Fenner ◽  
Gislaine Cristina Batistela ◽  
Danilo Simões
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Probability Distributions ◽  
Simulation Method ◽  
Forest Harvesting ◽  
Production Costs ◽  
Wood Harvesting ◽  
Food And Agriculture ◽  
The Monte Carlo Method

The processing of Eucalyptus logs is a stage that follows the full tree system in mechanized forest harvesting, commonly performed by grapple saw. Therefore, this activity presents some associated uncertainties, especially regarding technical and silvicultural factors that can affect productivity and production costs. To get around this problem, Monte Carlo simulation can be applied, or rather a technique that allows to measure the probabilities of values from factors that are under conditions of uncertainties, to which probability distributions are attributed. The objective of this study was to apply the Monte Carlo method for determining the probabilistic technical-economical coefficients of log processing using two different grapple saw models. Field data were obtained from an area of forest planted with Eucalyptus, located in the State of São Paulo, Brazil. For the technical analysis, the time study protocol was applied by the method of continuous reading of the operational cycle elements, which resulted in production. As for the estimated cost of programmed hour, the applied methods were recommended by the Food and Agriculture Organization of the United Nations. The incorporation of the uncertainties was carried out by applying the Monte Carlo simulation method, by which 100,000 random values were generated. The results showed that the crane empty movement is the operational element that most impacts the total time for processing the logs; the variables that most influence the productivity are specific to each grapple saw model; the difference of USD 0.04 m3 in production costs was observed between processors with gripping area of 0.58 m2 and 0.85 m2. The Monte Carlo method proved to be an applicable tool for mechanized wood harvesting for presenting a range of probability of occurrences for the operational elements and for the production cost.

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