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Monte Carlo analysis has been applied in calculation of geothermal resource capacity based on volumetric method issued by Standar Nasional Indonesia (SNI). A deterministic formula is converted into a stochastic formula to take into account the nature of uncertainties in input parameters. The method yields a range of potential power probability stored beneath Lawu Mount geothermal area. For 10,000 iterations, the capacity of geothermal resources is in the range of 139.30- 218.24 MWe with the most likely value is 177.77 MWe. The risk of resource capacity above 196.19 MWe is less than 10%. The power density of the prospect area covering 17 km² is 9.41 MWe/km² with probability 80%. [ABSTRACT FROM AUTHOR]

Among the essential steps to be taken in structural health monitoring systems, damage prognosis would be the field that is least investigated due to the complexity of the uncertainties. This paper presents the possibility of using Affine Arithmetic for uncertainty propagation of crack damage in damage prognosis. The structures examined are thin rectangular plates made of titanium alloys with central mode I cracks and a composite plate with an internal delamination caused by mixed mode I and II fracture modes, under a harmonic uniaxial loading condition. The model-based method for crack growth rates are considered using the Paris Erdogan law model for the isotropic plates and the delamination growth law model proposed by Kardomateas for the composite plate. The parameters for both models are randomly taken and their uncertainties are considered as defined by an interval instead of a probability distribution. A Monte Carlo method is also applied to check whether Affine Arithmetic (AA) leads to tight bounds on the lifetime of the structure. [ABSTRACT FROM AUTHOR]

The sensitivity of the steady-state average motor speed for Pulse Width Modulation (PWM) control of DC permanent magnetic motors is analyzed in this paper. Using Maple’s symbolic computation capability, an analytic form for the steady-state average motor speed is derived based on a first-principle model. Compared to previous result based on a MATLAB/Simulink model, this analytic form greatly reduces the time required to conduct statistical analysis. As a result, five hundred sets of randomly generated motor parameters are used to determine the standard deviation in the steady-state average motor speed as a function of the standard deviation in the motor parameters. This new approach allows for a detailed sensitivity analysis and the design feasibility study for motor PWM control. [ABSTRACT FROM AUTHOR]