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Statistical Estimation of Uncertainty in the MCRT Method

Excerpt

The Monte Carlo ray-trace (MCRT) method is based on a probabilistic interpretation of the radiative behavior of surface and volume elements, and the radiation distribution factor is itself a probability. Therefore, the uncertainty of results obtained using the method should be predictable using standard statistical methods. Specifically, we should be able to use statistical inference to state, to a specified level of confidence, the uncertainty of a result obtained. The chapter begins with a brief review of probability and statistics, after which the principles of statistical inference are applied to the MCRT method. Finally, a formal structure is presented for the experimental design of MCRT algorithms.

7.1Statement of the Problem
7.2Statistical Inference
7.3Hypothesis Testing for Population Means
7.4Confidence Intervals for Population Proportions
7.5Effects of Uncertainties in the Enclosure Geometry and Surface Models
7.6Single-Sample Versus Multiple-Sample Experiments
7.7Evaluation of Aggravated Uncertainty
7.8Uncertainty in Temperature and Heat Transfer Results
7.9Application to the Case of Specified Surface Temperatures
7.10Experimental Design of MCRT Algorithms
Problems
References
Topics: Uncertainty

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