国家能源委员会

This paper investigates the concept of metrological traceability in smart sensor networks used in hydraulic systems, which are fundamental for managing water, energy, and environmental resources. It particularly targets smart sensor networks permanently installed in flow conduits, analysing challenges related to sensor placement, in situ calibration, and long-term maintenance in complex, dynamic, and hard-to-access environments. Emphasising the need for application-specific strategies, the paper proposes harnessing recent advances in digital technologies, metrological frameworks, and autonomous systems. It outlines a hybrid strategy that integrates both fixed and mobile measurement standards with semi-blind in situ calibration methods and real-time monitoring capabilities implemented at the intelligent edge. These advancements aim to enhance measurement accuracy, system reliability, and traceability by allowing autonomous real-time recalibration, anomaly detection, and automated traceability management. This reduces reliance on human intervention while supporting the efficient and resilient operation of smart sensor networks in hydraulic systems. A central element of the proposed framework is the integration of Digital Metrological Twins, which would serve as virtual representations of measurement processes and support SI-traceable uncertainty propagation. The approach advocates deploying reduced-order models at the edge, synchronised with high-fidelity reference models in the cloud, to facilitate efficient, self-validating metrological workflows. By integrating perspectives from multiple disciplines, this work seeks to promote discussion and drive progress in achieving reliable measurement traceability for permanently embedded smart sensor networks in hydraulic systems.

Network reliability standards should ideally be informed by quantitative probabilistic models that quantify the costs and benefits associated with different levels of supply-quality improvement. However, these probabilistic models, such as those used to determine transmission network investments, require component-level outage and repair rates that are frequently missing or incomplete. To address this limitation, this paper proposes a two-part framework that first infers these missing input parameters and then delivers high-quality assessments of the investment costs required to attain a certain supply-quality level. In the first part, an Inverse Probabilistic Security-Constrained Optimal Power Flow (Inverse PSC-OPF) is developed to infer network-component reliability parameters—specifically outage and repair rates—directly from observed system-level continuity metrics routinely documented by regulatory authorities, namely interruption duration, interruption frequency, and unserved energy. In the second part, these calibrated parameters are integrated into a probabilistic transmission expansion planning model to find economically justifiable supply-quality improvements. We apply the framework to the Chilean transmission system ( $\approx ~2$ ,200 lines/transformers, $\approx ~2$ ,400 buses), where the resulting optimal investments, together with their costs and benefits across reliability levels, provided insights that informed the formulation of Chile’s national transmission reliability standard.

The Balanced Scorecard, developed in 1992 by Kaplan and Norton, has evolved into a communication and strategy execution system widely adopted by organizations across various industries. This article explores the use of an ontology to bridge the gap between strategy management and data within the Balanced Scorecard framework. The Balanced Scorecard Ontology is introduced to store, validate, and analyze knowledge, containing information about the strategy map and quantification frameworks, essential for evaluating the strategy execution. The proposed ontology is designed, developed, and evaluated using competency questions (CQs), and further validated by an online tool. Specifically, the proposed formalization of the Balanced Scorecard framework provides a semantic layer aimed at facilitating an effective Balanced Scorecard implementation, enabling accurate, traceable, and continuous monitoring and improvement of the strategy execution, based on a data-driven approach. The formalization of this knowledge through an ontology encompasses several advantages, such as improved interoperability and validation of the framework's elements, inference of new knowledge, and enhanced communication between different stakeholders. In addition, managerial implications include ensuring alignment between the Balanced Scorecard and organizational goals, supporting compliance and governance efforts, improving communication and knowledge transfer, enhancing the strategic decision-making process, and facilitating the integration of data into the Balanced Scorecard.

This paper describes the metrological testing of an electromagnetic velocity transducer, mainly used in forced vibration structural tests performed by LNEC's Concrete Dams Department. In addition to the brief description of this type of transducer and corresponding mathematical models, the metrological testing results are presented, aiming at the determination of the calibration constant, as well as the evaluation of measurement uncertainty. The obtained results showed a significant reduction of the transducer's total damping factor, critical damping resistance and sensitivity, which are reflected in the increase of the velocity resolution uncertainty component, being justified by antiquity and frequent use in field dynamic testing. The performed study also allowed the quantification of measurement uncertainties of input, intermediate and output quantities related to this velocity measurement approach. The output signal attenuation was identified as a major uncertainty contribution, therefore showing that particular attention must be given to the electrical calibration of the conditioning unit, also including the regulation of the external resistance.

This research presents a sensitivity analysis of various parameters that affect the carbonation of recycled aggregates (RAs), namely CO2 concentration, temperature, and relative humidity. The range of parameter values is close to that found in cement plant chimneys with regard to the forced carbonation of RAs. With this purpose, the main characteristics of flue gas streams (CO2 concentration, temperature, and relative humidity) from two Portuguese cement plants were identified and used in this research. The results indicated that temperatures around 60 °C and CO2 concentrations around 25% accelerate the carbonation reaction and increase CO2 absorption in mixed recycled aggregates (MRAs). CO2 absorption consistently decreased as the relative humidity was reduced from 60% to 40%. The highest amount of CO2 captured was by a recycled concrete aggregate (RCA) in the conditions of 23 °C, 60% RH, and 25% CO2. Overall, the RAs were able to capture a significant amount of CO2, ranging from 52 to 348 kg of CO2 per tonne of cement paste, depending on the nature of the RA. These findings drawn from a parametric campaign provide valuable insights into the potential enforcement of carbonation for recycled aggregates under conditions that closely reflect those found in cement plants.