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Abstract: This paper presents a comparative analysis of the influence of thermal loading on the design of optimally designed floating energy piles in soft consistency soils using a genetic algorithm. The nonlinear settlement of energy piles is also considered. The deterministic optimisation model (OPT-EP) includes a cost objective function constrained by design constraints and is later extended to include the probability of failure as a second objective function to perform multi-objective optimisation. This extension was undertaken because the Eurocode 7 approach only partially accounts for uncertainties in the soil, whereas the reliability-based design (RBD) approach fully exploits these uncertainties. Consequently, a multi-objective optimisation (cost vs. failure probability) was carried out in this study. The optimal designs obtained by the two different optimisation methodologies were further analysed and it was found that when the Eurocode 7 safety factor approach was used, the conditions related to thermal loading were not crucial for the design values. On the other hand, the multi-objective optimisation based on the RBD approach showed that the thermal loading affected the design, proving the usefulness of the multi-objective optimisation and the reliability-based design.
Keywords: energy pile, multi-objective optimisation, reliability-based design, genetic algorithm
Published in DKUM: 04.04.2025; Views: 0; Downloads: 5
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Keywords: multiple heat sources, Organic Rankine cycle, separate and cascade designs, Steam Rankine cycle, thermodynamic optimisation
Published in DKUM: 28.02.2025; Views: 0; Downloads: 2
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Abstract: Logistics and transport are major sources of energy consumption that still rely heavily on fossil fuels. Especially in the freight sector, finding means to optimise fuel consumption and energy efficiency are increasingly important. Digital twins’ adaptation in logistics and transport is not as frequent as in production, but their implementation potential is immense. This technology can replicate real environments, allowing verification of various scenarios without real-life application, leading to optimal implementation outcome faster and more efficiently. This paper aims to research digital twins’ use in logistics and transport, focusing on digital twins’ potential and effects to optimise energy consumption. Firstly, previous research on digital twins in specified fields was identified, followed by a quantitative literature review. The latter focused on codifying the selected publications based on criteria such as modality, specific environment, energy consumption, etc. Furthermore, a qualitative overview of 57 relevant publications on digital twins’ use in logistics and transport was made. The main contribution of this paper is the systematic overview of digital twins from the viewpoint of energy optimisation in one of the most energy-dependent sectors. This gives researchers a good starting point for further research and digital twins’ practical implementation cases.
Keywords: logistics, transport, digital twin, energy consumption, optimisation, literature review, bibliometrics
Published in DKUM: 22.04.2024; Views: 129; Downloads: 33
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Abstract: Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective.
Keywords: smart manufacturing, digital twin, optimisation, simulation modelling, Simio, case study
Published in DKUM: 25.03.2024; Views: 277; Downloads: 26
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Abstract: By integrating renewable energy sources into the existing distribution network, the characteristics and local stability of the network is highly impacted. The network, which was built with the goal of a directed energy flow from large conventional sources connected to the transmission network via the distribution network to consumers, can change the direction of the energy flow. The adoption of environmental commitments and directives encourages the integration of local dispersed energy sources, which can worsen voltage conditions in the distribution network. To avoid excessive local production, distribution network operators must limit the installation of new generati on units, as it is necessary to take into account the quality of power supply by monitoring its network parameters, such as the appropriate voltage profi le and the rati o between acti ve and reacti ve power. On the other hand, excessive loads due to the mass transiti on of household heati ng and transport towards electricity can also pose a problem for high-quality electricity supply due to the excessive voltage drop. The arti cle presents an algorithm for determining the maximum size of unit producti on and the maximum load at a node in the distributi on network. Also demonstrated is the use of variable tap transformer technology, which adjusts the tap of the transformer to provide an appropriate voltage profi le in the network. The enti re analysis was performed on a model of a real medium-voltage network, in which solar and hydropower plants are already included. The model was verifi ed by comparing its calculated values with actual measurements. The goal was to determine the size of the unit’s maximum producti on, as well as the size of the maximum load, by using the diff erenti al evoluti on algorithm, while keeping voltage profi les within the permissible limits. The results of the analysis are presented in the article.
Keywords: distribution network, renewable energy sources, optimisation method, voltage profile
Published in DKUM: 30.10.2023; Views: 425; Downloads: 17
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