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Supplementary Materials of the paper published in Open Research Europe : Connecting complex and simplified models of tipping elements: a nonlinear two-forcing emulator for the Atlantic meridional overturning circulation. Publication : https://doi.org/10.12688/openreseurope.19479.1

Supplementary Materials of the paper published in Open Research Europe : Connecting complex and simplified models of tipping elements: a nonlinear two-forcing emulator for the Atlantic meridional overturning circulation. Publication : https://doi.org/10.12688/openreseurope.19479.1

Abstract In the next 25 years an unprecedented number of new marine artificial structures, over 75,000 offshore wind turbines alone, are planned to meet global net zero targets. Structures are required to last for multiple decades in the hostile marine environment; where the largest cost across their whole lifecycle is on operations and maintenance dependent on accessibility in calm seas. However, the role of climate change on accessibility, and thus operational cost, has not been resolved. Here we provide the first study of future accessibility; evaluated from global climate model driven wave modelling, using the high emission scenario (RCP8.5). We found that climate change drives significant regional variation in accessibility, with the northern hemisphere benefiting from a 6% increase in operating windows whilst accessibility in parts of the southern hemisphere is reduced by 6-9%. These findings will help offshore developers and stakeholders incorporate adaptions to climate change as part of strategic planning practices.

Abstract In the next 25 years an unprecedented number of new marine artificial structures, over 75,000 offshore wind turbines alone, are planned to meet global net zero targets. Structures are required to last for multiple decades in the hostile marine environment; where the largest cost across their whole lifecycle is on operations and maintenance dependent on accessibility in calm seas. However, the role of climate change on accessibility, and thus operational cost, has not been resolved. Here we provide the first study of future accessibility; evaluated from global climate model driven wave modelling, using the high emission scenario (RCP8.5). We found that climate change drives significant regional variation in accessibility, with the northern hemisphere benefiting from a 6% increase in operating windows whilst accessibility in parts of the southern hemisphere is reduced by 6-9%. These findings will help offshore developers and stakeholders incorporate adaptions to climate change as part of strategic planning practices.

Slurry transportation is an essential process in numerous industrial applications, widely studied for its efficiency in material conveyance. Despite substantial research, the impact of pipe wall roughness on critical metrics such as pressure drop, specific energy consumption (SEC), and the Nusselt number remains relatively underexplored. This study provides a detailed analysis using a three-dimensional computational model of a slurry pipeline, with a 0.0549 m diameter and 3.8 m length. The model employs an Eulerian multiphase approach coupled with the RNG k-ε turbulence model, assessing slurry concentrations Cw = 40–60% (by weight). Simulations were conducted at flow velocities Vm = 1–5 m/s, with pipe roughness (Rh) ranging between 10 and 50 µm. Computational findings indicate that both pressure drop and SEC increase proportionally with roughness height, Vm, and Cw. Interestingly, the Nusselt number appears unaffected by roughness height, although it rises corresponds to Vm, and Cw. These insights offer a deeper understanding of slurry pipeline dynamics, informing strategies to enhance operational efficiency and performance across various industrial contexts.

Slurry transportation is an essential process in numerous industrial applications, widely studied for its efficiency in material conveyance. Despite substantial research, the impact of pipe wall roughness on critical metrics such as pressure drop, specific energy consumption (SEC), and the Nusselt number remains relatively underexplored. This study provides a detailed analysis using a three-dimensional computational model of a slurry pipeline, with a 0.0549 m diameter and 3.8 m length. The model employs an Eulerian multiphase approach coupled with the RNG k-ε turbulence model, assessing slurry concentrations Cw = 40–60% (by weight). Simulations were conducted at flow velocities Vm = 1–5 m/s, with pipe roughness (Rh) ranging between 10 and 50 µm. Computational findings indicate that both pressure drop and SEC increase proportionally with roughness height, Vm, and Cw. Interestingly, the Nusselt number appears unaffected by roughness height, although it rises corresponds to Vm, and Cw. These insights offer a deeper understanding of slurry pipeline dynamics, informing strategies to enhance operational efficiency and performance across various industrial contexts.

The present study investigates the enhancement of latent heat capacity and thermal stability in hybrid nano-enhanced solid–solid phase change materials (SS-PCMs) using Neopentyl Glycol (NPG) as the base material. The key contribution of this work lies in incorporating copper oxide (CuO) and titanium dioxide (TiO₂) nanoparticles to optimize thermal performance and ensure long-term stability. CuO (1 wt.%) and TiO₂ (0.1, 0.3, 0.5,0.7 wt%) were introduced into the matrix, and the thermal properties were systematically evaluated using Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) before and after 500 thermal cycles. The optimal composition, consisting of 1 wt% CuO and 0.3 wt% TiO₂, demonstrated an initial latent heat capacity of 117 J/g, which increased to 123 J/g post-cycling, indicating exceptional thermal stability and phase retention. To further enhance predictive capabilities and reduce experimental costs, an artificial neural network (ANN) model was developed using the Keras API in Python to estimate thermal behaviour. The model achieved a high coefficient of determination (R2 = 0.9479) and a low root-mean-square error (RMSE = 2.0307), underscoring its accuracy and reliability. These findings establish the efficacy of hybrid nanoparticle incorporation in improving SS-PCMs’ thermal properties and emphasise the viability of machine learning as a robust predictive tool, mitigating the time and economic constraints associated with extensive experimental investigations.