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Scaling up ceramic recovery from end-of-life solid oxide cells: Process optimization and evaluation of recovered materials
No full textThis study introduces a scalable and sustainable method for recovering yttria-stabilized zirconia (YSZ) and nickel (as NiO) from end-of-life (EoL) solid oxide cells (SOCs). The process combines hydrothermal disaggregation at 200 °C with acid leaching into a single-step treatment, enabling whole-cell recycling and eliminating the need for complex layer separation. Optimised conditions – 50 g of SOC powder treated with 1 M HNO3 for 1 h – achieved ≈92 wt% YSZ recovery while minimizing reagent use and processing time. The recovered YSZ showed a particle size distribution (445 ± 140 nm) comparable to virgin 3YSZ (470 ± 90 nm), with minimal Ni contamination (0.1 wt%) and preserved yttria content. When sintered at 1300 °C for 3 h, the material reached 95.5% relative density and an ionic conductivity of 7.9 × 10−3 S cm−1 at 800 °C, closely matching virgin 3YSZ (97.8%, 9.4 × 10−3 S cm−1). A residual monoclinic phase (17.4 wt%), which may slightly reduce transformation toughening, did not significantly affect ionic transport. Reuse pathways for recovered YSZ include closed-loop reintegration into SOC electrolytes or supports, and open-loop valorisation such as thermal barrier coatings or catalytic substrates. Concurrently, ≈99 wt% of Ni has been recovered in the form of NiO, with Co and La contamination below 1 wt%, further supporting circular economy strategies
AgriSmart: An IoT-enabled framework for agricultural resource optimization
No full textEfficient use of farming resources (e.g., nitrogen, water, pesticides) is key to maximizing productivity and promoting sustainable agriculture. Traditional methods, such as fixed-rate applications or soil sampling, often fail to adapt to changing in-season conditions and specific nutrient demands, leading to inefficiencies and environmental harm. In this work, we propose AgriSmart, an IoT-enabled framework that optimizes resource application strategies to maximize crop yield while minimizing resource usage within a given budget. AgriSmart formulates an optimization problem solved periodically using an enhanced Differential Evolution (DE) algorithm that balances exploration and exploitation, following a Model Predictive Control (MPC) approach. Crop yield responses to varying application timings and rates are estimated using the process-based crop simulation model DSSAT (Decision Support System for Agrotechnology Transfer). To improve flexibility and reduce computational complexity, we introduce adjustable receding horizon that allows multiple actions to be applied before re-optimization, enabling adaptation to resources with different application frequencies (e.g., water vs. nitrogen). As the time horizon advances, AgriSmart dynamically adjusts the resource applications to better match crop needs at each growth stage, responding to evolving weather and field conditions. We evaluate AgriSmart in two use cases: irrigation scheduling for soybean and nitrogen management for maize. Results show that AgriSmart outperforms existing methods, achieving up to 21.4% water savings for soybean without yield loss, and increasing maize yield by 20% while reducing nitrogen use by up to 32%
dynsight: An open Python platform for simulation and experimental trajectory data analysis
No full textThe study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as (i) identifying and tracking the constitutive objects/particles, resolving their trajectories (e.g., in experimental cases, where these are not automatically available as in typical molecular simulations); (ii) translating the trajectories into data that are easier to handle/analyze by using well-suited descriptors; and (iii) extracting meaningful information from such data. Each of these different tasks often requires non-negligible programming skills, the use of various types of representations or methods, and the availability/development of an interface between them. Despite the considerable potential that new tools contributed to each of these individual steps, their integration under a common framework would decrease the barrier to usage (especially by diverse communities of users), avoid fragmentation, and ultimately facilitate the development of new approaches in data analysis. To this end, here we introduce dynsight, an open Python platform that streamlines the extraction and analysis of time-series data from simulation or experimentally resolved trajectories.dynsight simplifies workflows, enhances accessibility, and facilitates time-series and trajectories data analysis, offering a useful tool for unraveling the dynamic complexity of a variety of systems (or signals) across different scales.dynsight is open source and can be easily installed using pip
Decarbonizzare i processi costruttivi e manutentivi delle infrastrutture stradali. Quadro di riferimento, strategie e pratiche tecniche.
Get PDFLa transizione verso infrastrutture stradali a basse emissioni di carbonio rappresenta oggi una priorità strategica per enti gestori, stazioni appaltanti e operatori del settore. L’evoluzione del quadro normativo internazionale, europeo e nazionale, insieme alla crescente attenzione degli stakeholder verso le istanze relative alla mitigazione dei cambiamenti climatici, ha determinato l’integrazione sistematica dei criteri di sostenibilità nei processi decisionali che governano l’intero ciclo di vita delle opere viarie.
Il presente report evidenzia come la riduzione dell’impronta carbonica delle infrastrutture stradali richieda l’adozione di processi, tecnologie e materiali, orientati alla minimizzazione dei consumi energetici, all’ottimizzazione dell’uso delle risorse e all’innalzamento dell’efficienza ambientale. Le fasi di pianificazione e progettazione assumono un ruolo determinante in tale percorso: è in queste fasi preliminari che si concentra il massimo potenziale di riduzione delle emissioni totali nel ciclo di vita, grazie alla possibilità di compiere le scelte strategiche più efficaci inerenti alla configurazione funzionale dell’opera e alle soluzioni tecniche per realizzarla. Tuttavia, gli interventi attuabili nella fase costruttiva o di gestione, pur presentando un potenziale di riduzione inferiore in termini relativi, sono ugualmente in grado di generare benefici significativi in termini assoluti, soprattutto in presenza di lavori a grande scala quali quelli stradali.
Sulla base di tali principi, il report sviluppa un quadro conoscitivo finalizzato a supportare l’elaborazione di strategie di decarbonizzazione applicabili all’intero ciclo di vita dell’infrastruttura stradale.
Il documento copre tre ambiti principali:
- Strategie per la decarbonizzazione, dal progetto alla gestione - si analizza il complesso quadro normativo multilivello e si approfondiscono i principi e i metodi della progettazione sostenibile, illustrando le modalità con cui i criteri ambientali vengono integrati nelle diverse fasi progettuali e presentando le strategie di monitoraggio e gestione delle prestazioni ambientali in esercizio.
- Materiali e tecnologie low carbon - si presenta, dopo aver esaminato il contributo emissivo dei materiali tradizionali, una mappatura delle soluzioni a ridotta intensità di carbonio, includendo tecnologie consolidate e innovazioni in fase sperimentale, adottabili ai fini del contenimento dell’impronta carbonica dell’opera.
- Impianti e cantieri - si approfondiscono le possibili misure per la riduzione delle emissioni generate dagli impianti di produzione e dalle attività di cantiere, includendo strategie per l’ottimizzazione della logistica, dei consumi e dei processi costruttivi.
Il documento si conclude con una serie di casi studio che illustrano esempi concreti di pratiche, soluzioni tecniche e strategie operative già applicate nel settore.
Il report è destinato a un ampio spettro di stakeholder — enti proprietari di strade e amministrazioni pubbliche, concessionarie, progettisti, imprese di costruzione, produttori di materiali e macchine operatrici, gestori di impianti — e si avvale del contributo del mondo accademico, nella convinzione che una collaborazione strutturata tra ricerca e industria costituisca un elemento imprescindibile per il progresso, la qualità e la sostenibilità del patrimonio infrastrutturale del nostro Paes
Chemical Reactor Network for Hybrid Rocket Engines Optimization
No full textThis study proposes the integration of a chemical reactor network into the design optimization procedure of hybrid rocket engines. The adoption of a chemical reactor network enables a more realistic representation of combustion phenomena compared to conventional equilibrium-based formulations, by embedding non ideal effects directly into engine design and performance evaluation. The combustion chamber is discretized into four perfectly stirred reactors (oxidizer-rich, fuel-rich, flame and mixer), implemented within the open-source framework Cantera. The network is trained using a particle swarm optimization algorithm against experimental data from the literature on liquid oxygen and paraffin-based wax propellants, demonstrating high accuracy and predictive capability. The trained chemical reactor network model provides a means to compute the actual characteristic velocity efficiency for each engine configuration considered during optimization, thus improving performance and mass computation during ascent integration. The proposed approach is applied to a reference hybrid rocket upper stage mission, comparing the performance with and without mixing enhancing devices. Results indicate that the chemical reactor network based framework offers a robust foundation for coupled engine–trajectory optimization, enhancing the physical consistency and reliability of hybrid rocket propulsion system design
Reframing Urban Morphology for AI: Integrating Qualitative Dataset for a Specialized Artificial Intelligence
No full textTo explore the potential of a customized GPT-4o language model in under-standing and analysing urban morphology theory, three influential books were
carefully selected: The Image of the City by Kevin Lynch (1960), Townscape by Gordon Cullen (1961), and The Death and Life of Great American Cities by Jane
Jacobs (1961). These books, cornerstones of urban form studies, were chosen for their shared historical context, thematic alignment, and methodological comple
mentarity. Starting from three prompts, the Concept Correlator AI was taught to describe the books through keywords and tables and connect the different words through diagrams. By leveraging this glossary, the model analysed prompts related to these concepts, drawing from a comprehensive collection of sources to provide well-informed, concise responses. It systematically compared definitions across the books uploaded in its folders, grouping similar ones under a unified category.
Through this refined glossary, the study evaluated AI’s ability to interpret urban forms concepts and identified gaps between the existing definitions and the responses of the customized GPT-4o model. Ultimately, this framework strengthened AI’s foundational knowledge, improving both the contextual accuracy of responses and computational efficiency in urban morphology research. Some definitions in the analysis were identified as incorrect and required reformulation of the lexicon by the AI. This targeted data organization enhances research efficiency by minimizing unnecessary data processing and improving the precision of AI-generated outputs. By bridging traditional typo-morphological theories with emerging AI methodologies, the study contributes to AI’s systematic and meaningful integration into urban morphology, advancing theoretical understanding and practical applications
Adopting Data-Driven Safety Management Strategy for Thermal Runaway Risks of Electric Vehicles: Insights from an Experimental Scenario
Get PDFThermal runaway (TR) of lithium-ion batteries (LIBs) represents a critical safety challenge in EV applications. This study explores the potential of data-driven safety management strategies for mitigating TR risks in EVs. To minimize the impact of external environmental factors on the degradation of LIBs, experiments were conducted using an accelerating rate calorimeter (ARC). The intrinsic thermal behavior of six nickel–cobalt–manganese (NCM) cells at different states of health (SOH) and operating temperatures has been captured in created adiabatic conditions. Multiple sensors were deployed to monitor the temperature and electrochemical and environmental parameters throughout the degradation process until TR occurred. The results show that both the thermal and electrochemical stability of LIBs have been affected, exhibiting consistent thermal patterns and early electrochemical instability. Furthermore, even under adiabatic conditions, the degradation of LIBs show synergistic effects with environmental parameters such as chamber temperature and pressure. Correlation analysis further revealed the coupling relationships between the monitored parameters. Through calculating their correlation coefficients, the results indicate advantages of combining thermal, electrochemical, and environmental parameters as being to characterize the degradation of LIBs and enhance the identification of TR precursors. These findings stress the importance of considering the battery-environment system as a whole in safety management of EVs. They also provide insights into the development of data-driven safety management strategies, highlighting the potential for achievement and integration of anomaly detection, diagnosis, and prognostics functions in current EV management frameworks
Validation of ERMES 20.0 finite element code for JET A2 antennas coupling studies
Get PDFThis study presents the validation of the finite element code ERMES 20.0, benchmarked against the well-established method-of-moments code TOPICA. The simulations focus on Ion Cyclotron Resonance Heating (ICRH) coupling for the JET A2 antennas. Validation is performed by comparing two key metrics: the scattering parameter matrix (S-matrix) and the electric field distribution in front of the antenna. These parameters are critical for assessing ICRH antenna-plasma coupling efficiency and understanding interactions with plasma-facing components. The results show a strong agreement between ERMES 20.0 and TOPICA, confirming the accuracy and reliability of the finite element approach. This comparative analysis highlights the capability of ERMES 20.0 to support advanced modeling of sheath rectification and wave–edge plasma interactions, which are central phenomena in ICRH system design and optimization
Soldering Si3N4 to Invar42 by a localized heating process
Get PDFThis study investigates the soldering process for joining silicon nitride (Si3 N4 ),a commonly used ceramic, to Invar42, a low thermal expansion alloy. The focuslies on analyzing interfacial reactions and evaluating the bonding performance ofthe Si3 N4 —Invar42 joint. Field emission scanning electron microscopy (FESEM)and energy-dispersive X-ray spectroscopy (EDS) were employed to examine themicrostructure, elemental distribution, and chemical composition of the interfa-cial region. The results underscore the critical role of interfacial reaction layersin the soldering process. Mechanical testing (single lap offset, SLO, lap sheartests) was conducted to assess the bonding strength and mechanical integrity ofthe soldered joints. Furthermore, the thermal stability and reliability of thesejoints were evaluated through SLO tests at 300◦C. This study contributes tothe advancement of a user-friendly, pressureless, localized heating and field-deployable technique for soldering Si3 N4 to Invar42, thereby facilitating thefabrication of advanced engineering systems in industrial environments
Hybrid Optimization Technique for Finding Efficient Earth–Moon Transfer Trajectories
Get PDFThe Lunar Gateway is a planned small space station that will orbit the Moon and serve as a central hub for NASA’s Artemis program to return humans to the lunar surface and to prepare for Mars missions. This work presents a hybrid optimization strategy for designing minimum-fuel transfers from an Earth orbit to a Lunar Near-Rectilinear Halo Orbit. The corresponding optimal control problem—crucial for missions to NASA’s Lunar Gateway—is characterized by a high-dimensional, non-convex solution space due to the multi-body gravitational environment. To tackle this challenge, a two-stage hybrid optimization scheme is employed. The first stage uses a Genetic Algorithm heuristic as a global search strategy, to identify promising feasible trajectory solutions. Subsequently, the initial solution guess (or guesses) produced by GA are improved by a local optimizer based on a Sequential Quadratic Programming method: from a suitable initial guess, SQP rapidly converges to a high-precision feasible solution. The proposed methodology is applied to a representative cargo mission case study, demonstrating its efficiency. Our numerical results confirm that the hybrid optimization strategy can reliably generate mission-grade quality trajectories that satisfy stringent constraints while minimizing propellant consumption. Our analysis validates the combined GA-SQP optimization approach as a robust and efficient tool for space mission design in the cislunar environment