Accuracy Level Of Results Research Articles

Thermochemical cooling for high-power chip using sodium acetate trihydrate/expanded graphite: A new strategy and model

This study uses thermochemical heat storage (TCHS) material comprising of sodium acetate trihydrate/expanded graphite (SAT/EG) to cool the high-power devices in enclosed space. Due to its extraordinary energy storage density of 785 J/g, SAT/EG effectively reduces the weight/volume of the cooling system. Comparing to using phase change materials, the dummy chip temperature can be maintained less than 160 °C for 18.7 % longer in time. Moreover, a novel thermogravimetric-thermochemical storage (TGTS) model is developed to describe the thermal management process of TCHS. New govern equation adopts the weightloss curve measured by thermogravimetric analysis and directly translates it as a process indicator of the decomposition, thereby omitting the need for correlations of conventional models developed from mechanism functions. As a result, the TGTS model exhibits notable advantages in terms of its simplicity, low programming difficulty, time-efficient preprocessing, and maintains the same level of result accuracy as conventional models (with an average error of 5.19 %). The model can theoretically guide the optimization of the thermal management system by achieving a balance between the decomposition enthalpy and thermal conductivity of SAT/EG under different cooling requirements.

A Multilevel Monte Carlo Method for Performing Time-Variant Reliability Analysis

Even though a great number of methods have been developed for time-variant reliability analysis (TRA), crude Monte Carlo simulation (MCS) is still widely used alone or combined with other methods to enhance the efficiency and accuracy of TRA. Multilevel Monte Carlo (MLMC) is introduced to TRA herein in order to reduce the computational complexity of MCS. MLMC first discretizes the time interval of interest by a geometric sequence of different timesteps. The finer grid is associated with smaller discretization error but larger computational cost. Accounting for the computational complexity of each level and the contribution that each level makes to the time-variant failure probability, the allocation of computational resource is then optimized in order to achieve a satisfying estimation of failure probability at the minimal computational cost. MLMC retains the robustness of crude MCS with respect to nonlinearity and dimensions of the time-variant performance function. Two benchmark examples are adopted to validate the effectiveness of the proposed MLMC. The results show that achieving the same accuracy level of results, the proposed method saves considerably computational cost relative to crude MCS.