MONTE CARLO SIMULATION OF THERMAL CONDUCTION THROUGH A RANDOMLY PACKED BED OF SPHERES

1982 ◽  
Author(s):  
Y. Sam Yang ◽  
John R. Howell ◽  
Dale E. Klein
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Thermal Conduction ◽  
Packed Bed

Robust Thermal Design for Power Device Package Using Response Surface Method and Monte Carlo Simulation

2007 ◽  
Author(s):  
Yasuyuki Yokono ◽  
Katsumi Hisano ◽  
Kenji Hirohata
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Thermal Resistance ◽  
Response Surface ◽  
Response Surface Method ◽  
Thermal Conduction ◽  
Thermal Design ◽  
Power Device ◽  
Design Parameters ◽  
Surface Method

In the present study, the robust thermal design of a power device package was accomplished using thermal conduction calculation, design of experiment, response surface method and Monte Carlo simulation. Initially, the effects of the design parameters on the solder strain were examined in terms of the thermal expansion difference as a result of unsteady thermal conduction simulation. From the factorial effects of design parameters, the design proposals were screened. Then, robustness of the thermal resistance was evaluated for the three design proposals obtained. The thermal resistances were calculated by solving the steady thermal conduction equation under the design of experiment conditions. The solder thickness, the substrate thickness, and the cooling fin performance were considered as the fluctuation factors, assuming the error associated with manufacturing process. Using a response surface method, the values of thermal resistance were expressed as a function of the design variables. The variances of the thermal resistance were examined based on Monte Carlo simulations. Related to the cooling fin design, the Pareto line showing the trade-off relation between the fin dimension and the fan velocity was obtained. By repeating the Monte Carlo simulations, the Pareto solution was calculated so that the thermal resistances satisfy the criteria in the position of 95 percrntile of the thermal resistance variation. Under the same flow velocity conditions, the fin dimensions become about 10% higher compared to the case where the manufacturing error was not taken into account. By carrying out the thermal design following this Pareto line, even if the manufacturing error was taken into consideration, the thermal resistance could satisfy the desired value.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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