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Reviewing numerical studies on sensible thermal energy storage in cementitious composites: report of the RILEM TC 299-TES
Full text linkPublisher Copyright: © The Author(s) 2024.Concrete has emerged as a promising solid-based sensible heat storage (SHS) material due to its favorable balance of thermal properties, cost-effectiveness, non-toxicity, and widespread availability. This state-of-the-art review examines the applications of concrete-based SHS across diverse domains, including buildings, concentrated solar power systems, and industrial power generation. It also investigates the thermal properties of concrete relevant for SHS applications and explores the design considerations for concrete SHS systems and reviews the current research landscape and the role of numerical modeling and simulation techniques in optimizing the performance of concrete SHS systems. Various computational methods, such as transient modeling, finite element method (FEM), computational fluid dynamics, and simplified lumped capacitance models, have been employed to analyze and enhance the design of these systems. As research and development continue in this field, several future trends are anticipated.Peer reviewe
Structure-Dependent HER Activity and Durability on Flat and Macroporous Ni–P Electrocatalysts in Acidic Medium
No full textPublisher Copyright: © 2025 The Authors. Published by American Chemical SocietyThe development of sustainable non-noble-metal catalysts for producing high-purity hydrogen via water electrolysis as an alternative to the state-of-the-art Pt-based materials has attracted considerable interest in recent years. Nevertheless, the widespread adoption of these catalysts remains limited due to their insufficient stability, particularly in an acidic environment. In this study, both flat and macroporous Ni–P alloy catalysts with controlled compositions and microstructures were synthesized via electroless deposition. The results demonstrate that increasing the phosphorus content up to 12 wt %, applying heat treatments, and utilizing macroporous templates significantly enhance catalytic activity for hydrogen evolution reaction, approaching that of Pt/C catalysts. Nevertheless, a trade-off between the catalytic efficiency and corrosion resistance was observed. Advanced characterization techniques, including scanning Kelvin probe force microscopy, revealed that heat-treatment-induced structural modifications play a crucial role in the catalyst degradation mechanism and can provoke the formation of local galvanic couples under negative polarization. These results offer important insights into the structure–property relationships of Ni–P alloys, highlighting their potential as efficient and durable HER catalysts in acidic media.Peer reviewe
A Bidding Algorithm for the Joint Participation of Distributed Energy Resources in Day-Ahead Energy and Mfrr Markets
No full textPublisher Copyright: © 2025 IEEE.This paper provides an optimization algorithm for the joint participation of an aggregator of flexible demand in the dayahead (DA) energy and manual frequency response (mFRR) markets. The algorithm, which is based on a mixed integer linear (MILP) optimization problem, defines the bids to be sent to the aforementioned markets, with the aim of minimizing the net cost for buying and selling energy in the DA market while maximizing the benefits from its participation in the mFRR market. This combined bidding strategy helps the aggregator to perform a better schedule of its flexibility resources, thus improving its revenue opportunities. The proposed bidding algorithm is tested in a realistic simulation case study based on the Portuguese pilot within the ELEXIA project comprising three office climatization systems and a photovoltaic generator. Results demonstrate the applicability of the developed algorithm to estimate the available flexibility and define the optimal multi-market bidding strategy.Peer reviewe
Advanced evaluation of heavy vehicle impact on flexible road viaduct expansion joints
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Experimentally monitoring the growth of surface-breaking cracks using digital image correlation technique integrated with permanently installed low-cost ultrasonic arrays in fatigue tests
No full textPublisher Copyright: © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).Monitoring crack growth is crucial for ensuring safety, assessing structural health, optimising maintenance strategies and advancing scientific knowledge in the field of structural engineering. It enables cost-effective asset management, proactive maintenance and the growth of innovative solutions for a safer and more sustainable built environment. In this paper, we explore various techniques to experimentally monitor the growth of surface-breaking cracks on a single edge notch Beam (SENB) specimen in four-point bending fatigue tests according to American Society for Testing and Materials (ASTM) standard E647-15. The notch was generated using electrical discharge machining (EDM). In the measurements, a 5 MHz low-cost ultrasonic array with 18 elements was permanently attached to the top surface of a structural steel (SS355) specimen. A clip gauge was installed around the EDM notch mouth to measure the crack mouth opening displacement (CMOD). Additionally, a digital camera was focused on the tip of the EDM notch to measure surface displacements using digital image correlation (DIC). During the four-point bending fatigue tests, experimental data sets were recorded from the ultrasonic array, the clip gauge and the digital camera at each loading cycle. We focused on the development of post-processing techniques for measuring crack length and CMOD using the acquired ultrasonic array datasets and the measured surface displacement and strain distributions from the DIC technique, as well as low-cost ultrasonic array design, fabrication and installation. The proposed post-processing techniques were validated by comparing the results obtained with calculations using the standard procedure in ASTM E1820 based on CMOD values acquired from clip gauge measurements. We demonstrated that both the low-cost ultrasonic array and DIC exhibit reasonable and reliable performance in detecting and monitoring crack growth. The developed post-processing techniques proved effective in accurately measuring key parameters of crack growth, such as crack length and CMOD. These findings validate the feasibility and applicability of using low-cost ultrasonic arrays and the associated post-processing techniques for crack monitoring and characterisation and for supporting structural integrity assessments.Peer reviewe
Neural quantum kernels: Training quantum kernels with quantum neural networks
Full text linkPublisher Copyright: © 2025 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Quantum and classical machine learning have been naturally connected through kernel methods, which have also served as proof-of-concept for quantum advantage. Quantum embeddings encode classical data into quantum feature states, enabling the construction of embedding quantum kernels (EQKs) by measuring vector similarities and projected quantum kernels (PQKs) through projections of these states. However, in both approaches, the model is influenced by the choice of the embedding. In this work, we propose using the training of a quantum neural network (QNN) to construct neural quantum kernels, specifically neural EQKs and neural PQKs - problem-inspired kernel functions. Unlike previous approaches, our method requires the kernel matrix to be constructed only once, significantly reducing computational overhead. To achieve this, we introduce a scalable training method for an n-qubit data reuploading QNN. Furthermore, we demonstrate neural quantum kernels can alleviate exponential concentration and enhance generalization capabilities compared to problem-agnostic kernels, positioning them as a scalable and robust solution for quantum machine learning applications.Peer reviewe
Temperature Estimation and Thermal Runaway Detection in a Novel Battery Module Arrangement Incorporating a Reduced Number of Sensors
No full textPublisher Copyright: © 2025 IEEE.Large battery packs are fundamental in transport electrification. Due to the safety-critical nature of these applications, continuous temperature monitoring is required. In addition, the rapid detection of hazardous events such as Thermal Runaway (TR) is desirable. In this work, a novel battery module arrangement, incorporating an aluminium sheet, is proposed. This novel configuration results in an homogeneous temperature distribution across the battery module. The conceptual design enables to estimate the surface and core cell temperatures, also providing fast detection of TR events with a reduced temperature sensor count. Finite Element Method (FEM) simulations are provided, showing the feasibility of the proposal.Peer reviewe
Steiner Traveling Salesman Problem with Quantum Annealing
No full textPublisher Copyright: © 2025 Copyright held by the owner/author(s).The Steiner Traveling Salesman Problem (STSP) is a variant of the classical Traveling Salesman Problem. The STSP involves incorporating steiner nodes, which are extra nodes not originally part of the required visit set but that can be added to the route to enhance the overall solution and minimize the total travel cost. Given the NP-hard nature of the STSP, we propose a quantum approach to address it. Specifically, we employ quantum annealing using D-Wave’s hardware to explore its potential for solving this problem. To enhance computational feasibility, we develop a preprocessing method that effectively reduces the network size. Our experimental results demonstrate that this reduction technique significantly decreases the problem complexity, making the Quadratic Unconstrained Binary Optimization formulation, the standard input for quantum annealers, better suited for existing quantum hardware. Furthermore, the results highlight the potential of quantum annealing as a promising and innovative approach for solving the STSP.Peer reviewe
Prediction horizon error analysis in thermal consumption models for control applications
No full textPublisher Copyright: © 2025 University of Split, FESB.Accurate consumption prediction models are crucial for optimizing building control applications, enhancing energy efficiency, reducing costs, and improving occupant comfort. However, prediction errors can significantly impact performance; overestimations lead to excessive energy consumption, higher operational costs, and increased carbon emissions, while underestimations result in inadequate heating, cooling, or lighting, negatively affecting comfort and productivity. This paper extends previous research by analysing the behaviour of prediction errors in six models of varying complexity. Using real consumption data from a large retail building in Madrid, the models predict energy demand across different time horizons, ranging from 1 hour to 24 hours. Results indicate that autoregressive models outperform others in short-term predictions but lose accuracy as the forecast horizon increases. Additionally, incorporating indexed parameters effectively mitigates error dispersion, improving model reliability over extended prediction periods.Peer reviewe
About the idea of a Responsible Artificial Intelligence
Full text linkPublisher Copyright: © 2025 Universidad de Valladolid. All rights reserved.The idea of Responsible Artificial Intelligence (RAI) has emerged in recent years as a reaction to the different challenges posed by the development of Artificial Intelligence (AI) in society. Through a comparative case study, including two campanies focused on the design and the development of AI systems in our country, the text argues that the RAI paradigm is still far from being popular outside these digital platforms and how the development of AI systems does not satisfactorily address the ethical and social implications associated with the deployment of AI systems.Peer reviewe