Cristina Montalvo

Since its publication by ISO in 1993, the GUM document has become the model to follow for the estimation of measurement uncertainty in the industry. However, the document itself recognizes its limitations in justifying the level of coverage. The calibration of volumetric meters for the custody transfer of liquid products can be affected by these limitations. The objective of this work is to calculate the calibration uncertainty of such meters by applying the Monte Carlo method of Supplement 1 of the GUM document (GUM-S1). In this way, the use of the classic GUM method is avoided. Therefore, the adaptive implementation of the Monte Carlo method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student’s t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand’s Probability Density Function (PDF) which results in a Flatten–Gaussian. This work proves that the GUM method does not assign the correct PDF. The uncertainty estimation is reduced by 7.1% compared to the GUM method.

The adaptive implementation of the Monte Carlo Method (AMCM) has been applied to optimize the calibration uncertainty of a positive displacement meter against a standard tank in an ISO 17025 accredited volume laboratory. These meters are used for custody transfer in liquid hydrocarbon logistics where any reduction in the uncertainty estimation may have an important economic impact. Several innovations are proposed when applying AMCM; regulation of AMCM convergence by applying the Student's t-distribution, validation of repeatability and filtering of outliers by performing 50 iterations of the AMCM, and characterization of the measurand's Probability Density Function (PDF) which results in a Flatten Gaussian. This work proves that GUM method does not assign the correct PDF. The uncertainty estimation is reduced 7.1 % compared to GUM method.

University students face situations throughout their careers that are sometimes difficult to overcome due to their lack of experience or preparation. Mentoring programs in university education have been proven to be a great accompaniment tool to solve these difficulties in addition to helping students’ personal and professional development. Our study aimed to show the complete mentoring process that accompanies students in the most crucial stages of their university education at the School of Mines and Energy of Universidad Politécnica de Madrid. Three stages of interest were identified in which mentoring projects have been implemented through training activities at the Unit of Social Entrepreneurship, Ethics and Values in Engineering (UESEVI). At the beginning of their studies, two welcome actions are offered: Mentor and Monitor projects that provide administrative and academic support; during the development of their studies, the Mentoring with Energy project offers professional tutoring that helps students in their incorporation into the labor market and provides them with tools to select a specialty; and in the last stage, professional accompaniment is provided by the Gray Angels project that helps students to develop their future careers. The activities of UESEVI are a strategy in our School to guarantee the development of soft skills and compliance with quality education processes. The usefulness of these programs can be measured through the participation and interest of the students. A positive evolution of these projects has been observed over the years. They have been growing according to the demand and needs detected in the successive evaluations of the programs.

The housing sector is one of the largest energy consumers in the world. There is an urgent need to renovate the housing stock of existing buildings. Therefore, it is necessary to correctly calculate the energy savings that can be obtained in a renovation project. The correct collection of energy data, the main variables that affect consumption, and people’s usage habits are fundamental elements to quantify the success or consequences that occur in an energy efficiency project. This research study quantifies the results of the energy savings of the European project DREEAM (District Scale Renovation for Energy Efficiency and Market Uptake). This article aims to facilitate the calculation of energy savings with mathematical linear regression models in two different climatic zones in Europe. Furthermore, it aims to improve the calculation of energy savings with mathematical models based on energy data and variables that affect consumption before and after renovations. The variables used for the calculation are hours of use, degree days, and reading days. Tenant behavior has been found to play an important role in actual measured savings. Additionally, the energy consumption patterns of the tenants are different after the renovations.

KWU-PWR reactors (SIEMENS design) are commonly exhibiting high neutron noise levels that can lead to costly operational issues. Recent analysis seems to indicate that, coolant flow, temperature oscillations, and mechanical vibrations have a key impact on neutron noise phenomena. In order to advance in understanding this phenomenon, the transient nodal code SIMULATE-3K (S3K) has been already utilized to simulate scenarios with individual or combined types of perturbation sources: mechanical vibrations of fuel assemblies and thermal-hydraulic fluctuations at the core inlet. In this work, new simulations are performed with all the perturbations applied simultaneously. The simulated neutron detectors responses are then analyzed with noise analysis techniques. All the simulated spectral features of neutron noise are compared to those obtained from real plant data. Results show that the simulated neutron noise phenomenology behaves similarly to that obtained from real plant data by increasing the fluctuation amplitude in the inlet coolant flow in the S3K calculations.

Summary: In dynamic, complex and nonlinear systems, the analysis of transients, instabilities or unusual events is fundamental, as well as the knowledge of the system's behavior in the frequency space. What is usual in this type of study is to have discrete and digitally acquired data, due to a sampling process with a determined period or frequency. Extracting information from this data set is done by numerical methods, such as the Fourier Transform (FFT), the Hilbert‐Huang Transform (HHT) or techniques from chaos mathematics (Attractors). In this way we have scarce data, with some loss of information, analyzed with numerical techniques that introduce, also, some dilution of the information. In particular, the techniques of analysis in the frequency space, by their very nature, blur, frequencies and bandwidths. This leads in some cases to the identification of unsuitable or very wide frequencies or intervals, which make the extracted information unusable. To this effect, we have studied the VMD technique that provides more real and representative frequencies of the system as well as narrower bandwidths that make possible better identification of phenomena and influences. This technique has been applied to the analysis of thermohydraulic instabilities in nuclear boiling water reactors (BWR). The cases had already been analyzed with STFT and HHT techniques. The results of the application of the VMD shows that the mode mixing is solved, so modes obtained are monofrequency and these frequency bands are narrower than in previous methodologies. This improved performance allows using backbone plots for identifying which modes are excited in instabilities events.