Skip Nav Destination
The Monte Carlo Ray-Trace Method in Radiation Heat Transfer and Applied Optics
ISBN:
9781119518518
No. of Pages:
280
Publisher:
ASME Press
Publication date:
2019
eBook Chapter
7 Statistical Estimation of Uncertainty in the MCRT Method
Page Count:
28
-
Published:2019
Citation
Mahan, JR. "Statistical Estimation of Uncertainty in the MCRT Method." The Monte Carlo Ray-Trace Method in Radiation Heat Transfer and Applied Optics. Ed. Mahan, JR. ASME Press, 2019.
Download citation file:
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.
Topics:
Uncertainty
7.1
Statement 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
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Uncertainty in Fire Protection Engineering Design
Uncertainty in Fire Standards and What to Do About It
Uncertainty in Ductile Fracture Initiation Toughness ( J Ic ) Resulting From Compliance Measurement
Application of Automation Technology in Fatigue and Fracture Testing and Analysis
Assessment of Uncertainty in Reactor Vessel Fluence Determination
Reactor Dosimetry
The Role of Risk Assessment in the Regulation of Pesticides
Pesticide Formulations and Application Systems: 10th Volume
Related Articles
Model-Form Calibration in Drift-Diffusion Simulation Using Fractional Derivatives
ASME J. Risk Uncertainty Part B (September,2016)
Semi-Analytic Probability Density Function for System Uncertainty
ASME J. Risk Uncertainty Part B (December,2016)
Learning an Eddy Viscosity Model Using Shrinkage and Bayesian Calibration: A Jet-in-Crossflow Case Study
ASME J. Risk Uncertainty Part B (March,2018)