https://repos.hcu-hamburg.de:443/handle/hcu/15 2026-09-17T23:02:00Z 2026-09-17T23:02:00Z Pourbozorgi Langroudi, Pakdad Weidlich, Ingo Hay, Stefan https://repos.hcu-hamburg.de:443/handle/hcu/1286 2026-09-12T00:05:53Z 2026-09-11T09:40:07Z

Title: A Review and Classification of Ageing Models of Ageing Models for District Heating Pipelines Authors: Pourbozorgi Langroudi, Pakdad; Weidlich, Ingo; Hay, Stefan Abstract: District Heating (DH) systems have been an essential component of urban energy infrastructure for nearly a century, particularly in regions with severe winter climates, such as Northern Europe and the Baltic states. These networks reliably deliver thermal energy to residential, institutional, and industrial consumers. Like all infrastructure systems, the operational lifespan of DH pipelines is finite and is largely determined by material properties, operating conditions, and installation quality. To ensure long-term serviceability, investment planning, and effective asset management, it is critical to understand the ageing mechanisms of DH pipelines and to develop robust models for service life prediction and Remaining Useful Life (RUL) estimation. The European standard EN 253 provides quality assurance guidelines and minimum performance thresholds—typically specifying a 30-year minimum service life under defined operating conditions—for pre-insulated bonded pipe systems. While such standards offer valuable product-level benchmarks, they do not provide sufficient tools for predicting infrastructure-wide lifetime or RUL during operation. Existing literature classifies the ageing models as deterministic or probabilistic approaches, which they are respectively based on material science–based models, and on failure event statistics. These approaches are often described respectively as “Top-Down” (statistical) and “Bottom-Up” (material-based) methods. More recently, the emergence of machine learning and the increasing availability of operational and failure data have enabled the development of advanced data-driven models with improved predictive capabilities for both failure forecasting and remaining useful life estimation. This paper presents a critical review and updated classification of ageing models for DH pipelines, organizing them into three main categories: deterministic, probabilistic, and data-driven approaches. The advantages and limitations of each class are discussed, with particular emphasis on their applicability to real-world DH network management and remaining useful life prediction. The proposed framework aims to support the development of integrated hybrid models that can better capture the complex, multifactorial ageing processes in DH systems and improve long-term infrastructure planning and maintenance strategies.

2026-09-11T09:40:07Z Fakharizadehshirazi, Elham Rezagholi, Reza Rösch, Christine Peters, Irene https://repos.hcu-hamburg.de:443/handle/hcu/1285 2026-09-12T00:05:38Z 2026-09-11T09:19:44Z

Title: Where to harvest solar energy in Iran? A geographic information system (GIS) analysis for supporting the siting of photovoltaic (PV) parks and concentrating solar power (CSP) plants Authors: Fakharizadehshirazi, Elham; Rezagholi, Reza; Rösch, Christine; Peters, Irene Abstract: Iran's electricity generation relies heavily on fossil fuels, resulting in frequent power shortages and widespread blackouts in major cities. Given the high levels of solar irradiance across the country, photovoltaic (PV) and concentrating solar power (CSP) technologies could provide a sustainable alternative. Existing studies focus on specific technologies or individual regions. Currently, there is no consistent, comprehensive mapping of the scope for political decision-making in Iran. This study aims to address this issue by providing the first nationwide assessment of solar energy potential in Iran, evaluating both PV and CSP. This GIS-based assessment uses an expanded set of environmental and technical criteria and performs sensitivity analyses to ensure robust results and identify the most effective and sustainable locations for PV and CSP plants. The model incorporates specific constraints, such as protected natural areas, to exclude unsuitable sites, and assesses suitability based on criteria such as solar irradiation levels and proximity to grid infrastructure. These factors are categorised into four suitability classes, ranging from 'high' to 'very low' for both PV and CSP installations. By synthesising the constraint and suitability maps, the model identifies feasible sites and assesses their relative desirability. A sensitivity analysis, focusing on the weighting of the suitability criteria, confirms the robustness of the results. The results highlight Iran's considerable capacity for solar power generation and suggest that the country could exceed its current electricity production by a multiple through the development of solar power plants. The model applies 14 exclusion criteria, revealing that 70% of Iran’s land is unsuitable for PV and 83% for CSP. The results show that 14.5% of Iran’s land is suitable for PV and 7.5% for CSP (medium and high suitable), with central and eastern regions offering the highest potential. Additionally, the study highlights the promising prospects of GIS modeling in renewable energy siting, emphasizing improved data integration, global scalability, environmental impact assessment, and policy harmonization.

2026-09-11T09:19:44Z Manivannan, Ajaykumar Spaiser, Viktoria Cann, Tristan J. B. Evans, James Everall, Jordan P. Falkenberg, Max Garcia, David Guo, Weisi Herzog, Rico Otto, Ilona M. Oswald, Yannick Pagan, Nicolò Pellert, Max Pilgrim, Charlie Rodriguez-Pardo, Carlos Sen, Indira Vezhnevets, Alexander Sasha https://repos.hcu-hamburg.de:443/handle/hcu/1284 2026-09-12T00:04:03Z 2026-09-11T09:05:45Z

Title: Generative AI for climate governance and acceptability-constrained policy design Authors: Manivannan, Ajaykumar; Spaiser, Viktoria; Cann, Tristan J. B.; Evans, James; Everall, Jordan P.; Falkenberg, Max; Garcia, David; Guo, Weisi; Herzog, Rico; Otto, Ilona M.; Oswald, Yannick; Pagan, Nicolò; Pellert, Max; Pilgrim, Charlie; Rodriguez-Pardo, Carlos; Sen, Indira; Vezhnevets, Alexander Sasha Abstract: Climate policies often fail when they clash with cultural values, social identities, and fairness perceptions. We propose Acceptability-Constrained Climate Policy Design (ACCPD), using large language models as “cultural world models” to simulate public responses before implementation. By embedding LLMs in generative agent-based models and physical system simulators, ACCPD aims to enable policymakers to co-optimize for climate-policy efficacy and social legitimacy. We discuss methodological limitations regarding representation and LLM opacity.

2026-09-11T09:05:45Z Li, Weilian Zhu, Jun Zhang, Jinbin Wu, Jianlin Zhu, Qing Dehbi, Youness https://repos.hcu-hamburg.de:443/handle/hcu/1282 2026-09-17T19:13:55Z 2026-09-11T08:24:25Z

Title: Spatial modelling for bridge construction environment towards geospatial digital twin Authors: Li, Weilian; Zhu, Jun; Zhang, Jinbin; Wu, Jianlin; Zhu, Qing; Dehbi, Youness Abstract: Geospatial digital twin (GDT) presents an opportunity to map the physical geographic environment into the virtual geographic environment, offering a new perspective towards intelligent construction. However, the application of GDT in engineering construction is still in its infancy. There is a gap from the concept idea to system implementation and primary application, particularly in bridge construction. Following the connotation of GDT, this article proposes a spatial modelling approach for bridge environments aimed at supporting intelligent bridge construction. The logic model, numerical simulation, spatial modelling, and visual representation involved in bridge digital construction are discussed in detail. Finally, we developed a WebGL-based prototype system and selected a mega suspension bridge under construction as the case for experimental analysis. The experimental results demonstrate that the proposed approach can effectively support the generation of 3D scenes of the bridge environment and the visual simulation of the bridge construction. Specifically, numerical simulation of multi-field coupling and spatial modelling of the bridge environment are beneficial for pre-discovering hidden risks in construction, and the visual simulation of bridge construction could serve as a digital window to assist in upgrading intelligence in construction. The proposed method aligns with the concept of GDT and offers a tailored solution for modelling virtual geographic environments in the context of GDT.

2026-09-11T08:24:25Z