A new computational model for entire pulse tube refrigerators: Model description and numerical validation

In present paper, a new modeling approach for the performance of pulse tube refrigerator is proposed. The new approach combines one-dimensional and two-dimensional models (1-D and 2-DCC model) together, and can be used to simulate the fluid flow and heat transfer processes of the basic type, orifice type and double-inlet type pulse tube refrigerators (PTRs). With the present model, the complicated fluid flow and heat transfer characteristics in the PTR system can be efficiently depicted and the computational time can be greatly reduced. Then based on the approach, the distribution characteristics of the flow and temperature fields of the three types of PTR are numerically analyzed. The complicated fluid flow and heat transfer phenomena in PTR, such as DC flow, velocity and temperature annular effects are presented vividly. The numerical results show that the 1-D and 2-DCC model is reliable and practical, which can be used to explore the physical mechanism of the thermodynamic processes of the PTR system and optimize the design of the PTR system and its components.     

Integration of computation and testing for reliability estimation

This paper develops a methodology to integrate reliability testing and computational reliability analysis for product development. The presence of information uncertainty such as statistical uncertainty and modeling error is incorporated. The integration of testing and computation leads to a more cost-efficient estimation of failure probability and life distribution than the tests-only approach currently followed by the industry. A Bayesian procedure is proposed to quantify the modeling uncertainty using random parameters, including the uncertainty in mechanical and statistical model selection and the uncertainty in distribution parameters. An adaptive method is developed to determine the number of tests needed to achieve a desired confidence level in the reliability estimates, by combining prior computational prediction and test data. Two kinds of tests — failure probability estimation and life estimation — are considered. The prior distribution and confidence interval of failure probability in both cases are estimated using computational reliability methods, and are updated using the results of tests performed during the product development phase.