José Mira

Reliability analysis for systems with large hydro resources in a deregulated electric power market

This work describes a procedure that determines the optimal allocation for the yearly energy resulting from random water inflows to the different subperiods of a year so that the expected benefits are maximized. Its main idea is to distribute the energy stored in reservoirs in each period into two parts: one is directly sold in the energy market, while the other is made available to cover any unplanned outages of thermal units. The method proposed fulfills two objectives, to distribute the hydro energy optimally according to economic criteria and to assess the impact of new market rules on the reliability of an electric system. The procedure will be illustrated by an example based on the Spanish generating system.

Student academic performance stochastic simulator based on the Monte Carlo method

Abstract In this paper, a computer-based tool is developed to analyze student performance along a given curriculum. The proposed software makes use of historical data to compute passing/failing probabilities and simulates future student academic performance based on stochastic programming methods (Monte Carlo) according to the specific university regulations. This allows to compute the academic performance rates for the specific subjects of the curriculum for each semester, as well as the overall rates (the set of subjects in the semester), which are the efficiency rate and the success rate. Additionally, we compute the rates for the Bachelors degree, which are the graduation rate measured as the percentage of students who finish as scheduled or taking an extra year and the efficiency rate (measured as the percentage of credits of the curriculum with respect to the credits really taken). In Spain, these metrics have been defined by the National Quality Evaluation and Accreditation Agency (ANECA). Moreover, the sensitivity of the performance metrics to some of the parameters of the simulator is analyzed using statistical tools (Design of Experiments). The simulator has been adapted to the curriculum characteristics of the Bachelor in Engineering Technologies at the Technical University of Madrid (UPM).

Prediction of deterministic functions: an application of a Gaussian kriging model to a time series outlier problem

Univariate kriging is used in approximating the sample distributions of statistics for detecting outliers in time series. Two experimental designs are compared, a traditional factorial design and a maximum entropy design. In both cases, the results, in terms of prediction errors, are satisfactory. The computing times for the predictors imply a reduction of two orders of magnitude in comparison to a Monte Carlo procedure.

Long-term generation scheduling in systems with large hydro resources in a deregulated electric power market

This work describes a procedure that determines the optimal allocation for the yearly energy resulting from random water inflows to the different subperiods of a year so that the expected benefits are maximized. Its main idea is to distribute the energy stored in reservoirs in each period into two parts: one is directly sold in the energy market, while the other is made available to cover any unplanned outages of thermal units. The method proposed fulfills two objectives, to distribute the hydro energy optimally according to economic criteria and at the same time to maximize the reliability of the system. The procedure is illustrated by an example based on the Spanish generating system.

A MELCOR Application to Two Light Water Reactor Nuclear Power Plant Core Melt Scenarios with Assumed Cavity Flooding Action

Abstract The MELCOR 1.8.4 code Bottom Head package has been applied to simulate two reactor cavity flooding scenarios for when the corium material relocates to the lower-plenum region in postulated severe accidents. The applications were preceded by a review of two main physical models, which highly impacted the results. A model comparison to available bibliography models was done, which allowed some code modifications on selected default assumptions to be undertaken. First, the corium convective heat transfer to the wall when it becomes liquid was modified, and second, the default nucleate boiling regime curve in a submerged hemisphere was replaced by a new curve (and, to a much lesser extent, the critical heat flux curve was slightly varied). The applications were devoted to two prototypical light water reactor nuclear power plants, a 2700-MW(thermal) pressurized water reactor (PWR) and a 1381-MW(thermal) boiling water reactor (BWR). The main conclusions of the cavity flooding simulations were that the PWR lower-head survivability is extended although it is clearly not guaranteed, while in the BWR sequence the corium seems to be successfully arrested in the lower plenum. Three applications of the CFX 4.4 computational fluid dynamics code were carried out in the context of the BWR scenario to support the first modification of the aforementioned two scenarios for MELCOR. Finally, in the same BWR context, a statistic predictor of selected output parameters as a function of input parameters is presented, which provides reasonable results when compared to MELCOR full calculations in much shorter CPU processing times.

Analytical results for a Bayesian bivariate cokriging model

In this paper, we extend the results of Handcock and Steins univariate Bayesian kriging model to a bivariate response, integrating out analytically the two stationary variances.

The Use of Dimensional and Similarity Analyses in the Propagation of Uncertainties: A Physical Example

Dimensional and similarity analyses are used in physics and engineering, specially in fluid mechanics, to reduce the dimension of the input variable space with no loss of information. Here, we apply these techniques to the propagation of uncertainties for computer codes by the Monte Carlo method, in order to reduce the variance of the estimators of the parameters of the output variable distribution. In the physics and engineering literature, dimensional analysis is often formulated intuitively in terms of physical quantities or dimensions such as time, longitude, or mass; here we use the more rigorous and more abstract generalized dimensional analysis of Moran and Marshek. The reduction of dimensionality is only successful in reducing estimator variance when applying variance-reduction techniques and not when using ordinary random sampling. In this article we use stratified sampling, and the key point of the success of the reduction in dimensionality in improving the precision of the estimates is a better measurement of the distances betwen the outputs, for given inputs. We illustrate the methodology with an application to a physical problem, a radioactive contaminant transport code. A substantial variance reduction is achieved for the estimators of the mean, variance, and distribution function of the output. Last, we present a discussion on which conditions are necessary for the method to be successful.