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Premixed hydrogen-methane combustion modelling with ECFM in a pre-chamber equipped RCEM
Get PDFHydrogen is currently being investigated as an energy vector to decarbonize the transport sector, leveraging its
numerous advantages as a fuel for internal combustion engines. This study builds on an experimental campaign
aimed at assessing the impact of hydrogen enrichment in methane mixtures. Specifically, it evaluates the influence
of hydrogen in a fully premixed, passive pre-chamber-equipped, spark-ignition Rapid Compression
Expansion Machine (RCEM) under varying equivalence ratios and hydrogen contents. Additionally, the experimental
dataset serves to validate the predictive capability of the flamelet-based Extended Coherent Flame Model
(ECFM) in RANS numerical simulations.
Overall, the model demonstrates solid predictive capabilities, successfully capturing the experimental trends
across all mixture compositions. In particular, it accurately predicts stoichiometric cases with hydrogen content
ranging from pure hydrogen to a 25% volumetric ratio in methane. However, in lean cases (φ = 0.625), the
model exhibits greater discrepancies, requiring more extensive point-to-point calibration of the alpha stretch
parameter. These discrepancies, along with the need for point-by-point calibration, are closely linked to the
adaptability of the combustion model when the combustion regime is modified, due to variations in the chemical
properties of the fuel blend. Furthermore, the pre-chamber system introduces different combustion processes as
the flame is propagated from the pre-chamber to the main chamber, where combustion conditions differ
significantly. The turbulence-chemistry interaction was analysed to link the calibration process to the combustion
regime, using a detailed examination of the Borghi–Peters diagrams. Complementarily, two different kinetic
mechanisms were employed to generate laminar flame speed values, enabling an assessment of the reliability of
kinetic descriptions in parallel with turbulence–flame interaction modelling, thereby providing a more robust
overall analysis
Artificial intelligence and its impact on the current and future transport workforce: policies and research to bridge the gaps and foster cooperation versus competition between countries
No full textArtificial Intelligence (AI) is widely recognised as the revolution of this era; however, its adoption is uneven across industries and countries, with a wide gap between Europe and non-EU countries.
The paper analyses the state of AI adoption, focusing on transport workforce, and aims at proposing common actions, research, and policies to create cooperation rather than competition.
To this end, a literature review was conducted, followed by two field surveys on the needs and actions of stakeholders and students to manage the transition to increasing digitalisation and automation accelerated by AI. The field surveys involved focus groups, interviews and archaeological ethnography and were based on a worldwide selection of stakeholders from different transport sectors, and a sample of undergraduate, master's, and PhD students. Textual analysis was used for data analysis.
Over 900 stakeholders from 45 countries, including all levels of workforce, and nearly 600 students were involved. Needs, issues and concrete actions emerged, proposing specific measures to reduce the risks generated by AI. The main recommendations refer to: a) labour market regulation that requires broader inclusion of social dialogue; b) integration of the educational approach at different school and work levels, to prepare people to think independently and be creative in order to prevent shocks in adapting to AI evolution, fostering collaboration instead of competition. Field experiments are proposed to test policies, making the transition to AI use effective. The EU-US comparison in light of the proposed policies highlights the barriers created by different economic contexts, values and market regulations
Edge-to-Cloud Continuum Made Real
No full textDespite the number of projects and initiatives aiming at creating the so-called computing continuum, surprisingly no commonly adopted definitions exist so far. This article proposes a definition of the computing continuum that relies on three transparency properties, namely orchestration, communication, and resource availability
Energy Performance Certificates and Housing Transaction Prices: Empirical Evidence from Market Data Analysis
No full textIn recent years, the intersection between real estate market dynamics and environmental sustainability has gained increasing attention, particularly in light of the European Union’s regulatory efforts to decarbonize the building sector. Energy Performance Certificates (EPCs), initially conceived as informational tools, are now emerging as significant drivers of housing prices and investment decisions. This study investigates the impact of EPCs on real estate transaction prices within the urban context of Turin, Italy, with a specific focus on spatial effects often neglected in traditional analyses. Using a dataset of over 5,000 property listings from 2022–2023, a Spatial Error Model (SEM) is implemented to capture spatial autocorrelation and improve model accuracy. The results reveal a statistically and economically significant “green premium” for high-efficiency dwellings (EPC A and B–C) and a corresponding “brown discount” for inefficient properties (EPC F–G). These findings not only validate the role of EPCs in price formation but also align with the broader objectives of the EU Green Deal and the Green Asset Ratio (GAR), which increasingly integrates energy performance into financial valuation. The study offers novel insights into the spatial valuation of sustainability and suggests that market forces, coupled with regulatory instruments, are reshaping investment patterns in the built environment
Valuing the contribution of green roofs to pluvial flood risk mitigation: A cost-benefit analysis
No full textCities in the 21st century face increasing pressures from population growth, urban sprawl, and emissions, while
flood-related challenges exacerbated by climate change strongly intensify their vulnerabilities. Consequently,
urban global change is becoming an urgent necessity globally. Nature-based solutions (NBS) have gained
increasing attention as valuable sources of ecosystem services, which can also address these multiple societal
challenges. Green roofs are widely used for stormwater management and treatment in compact urban environments.
However, to date, most research has been conducted regarding green roof costs or flood risk mitigation
benefits at a citywide scale; while local administrations need more evidence on the economic viability of green
roofs that may increase the willingness to consider these nature-based solutions. Hence, the objective of this
study is to develop and apply a spatially explicit assessment of flood risk mitigation impacts (biophysically –
water depth), costs and benefits of NBS (economically – implementation costs, avoided damage costs, net present
values and benefit-cost ratios) under current (2013) and future (2050; RCP 4.5) climate conditions – with a case
study for green roofs in Rapallo (Italy). The spatial biophysical-economic approach integrates the InVEST Urban
Flood Risk Mitigation model (spatial resolution: 5 m × 5 m), benefit transfer methods, and geographic information
systems into a cost-benefit analysis. Results show that flood risks under current (2013) climate conditions
imply significant building damage costs (~6.5 million €/yr for Rapallo), that these costs increase when
considering future (2050) climate conditions (by about 7 %), and that NBS (green roofs) implementation can
reduce these costs (by almost 90 %). Moreover, green roofs result to be economically viable from a flood
mitigation perspective alone when considering Low NBS costs, while flood mitigation benefits contribute to,
respectively, 87 % and 63 % of the green roof annual implementation costs when considering Medium and High
NBS costs. Finally, results show that the economic viability of green roofs differs across neighbourhoods – hence
allowing for the economic prioritization of green roof implementation across neighbourhoods. By quantitively
assessing NBS impacts, costs, and benefits at the neighbourhood level, this study supports the decision on the
most viable locations for the implementation of NBS for flood risk mitigation – highlighting the need for spatial
assessment studies to support urban NBS development strategies
Decision-Making CIA Process (DeMaCIA) to Support SNAI Strategies: The Case of Castelluccio Inferiore in Basilicata, Italy
No full textThe debate on Inner Areas has produced the SNAI strategy (since 2014). It involves public and private actors, aiming to reverse the dynamics of small towns’ depopulation and the citizenry’s inaccessibility to primary services. We configured an intervention project for the Lucanian territory with an interdisciplinary approach and evaluated it through scenario simulation (DeMaCIA). The study is realized within an Agreement between the Municipality of Castelluccio Inferiore (PZ) and the Polytechnic University of Turin (2020–2025). Analyzing
the use of resources deployed over the past 10 years (SNAI and PNRR) reveals a lack of population involvement. While identifying possible strategies to trigger active resident participation, we hypothesized the configuration of a Renewable Energy Community (REC) embedded in the site of the Old Mills, as a way to
enable the collective sharing of the environmental and cultural resources of their area. The paper focuses on the methodology adopted by building a process to be applied in other fragile contexts
Improving fidelity of close social interaction animations in social VR with a machine learning-based refinement framework
No full textSocial Virtual Reality platforms enable users to embody avatars and interact in virtual worlds. While research suggests that full-body avatar representations are generally preferred, high behavioral fidelity in avatar animations can be hard to achieve. Hardware-based tracking can be particularly effective but is costly, whereas Inverse Kinematics (IK) is more affordable but less accurate, leading to less realistic motion. Recent neural network-based approaches have shown promise in improving IK-based animations by predicting natural movements; however, guaranteeing high levels of fidelity in avatar-to-avatar interactions, particularly those involving close contact, remains challenging even with those approaches. With the aim to address such issue, this paper proposes a neural network-based refinement framework to enhance behavioral fidelity in close social interactions. To investigate its effectiveness, hugging has been selected as a use case. The framework, trained on motion capture data, has been evaluated via a user study, showing improved behavioral fidelity in avatar social interactions
Valorization of waste streams for aerospace composites production
No full textL'abstract è presente nell'allegato / the abstract is in the attachmen
Preliminary Design and Testing of Brush.Q: An Articulated Ground Mobile Robot with Compliant Brush-like Wheels
Get PDFRecent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains