Modelling of District Heating Systems: Comparative Evaluation of White-box Modelling Approaches

District heating systems are prevalent in most European countries, and such energy delivery methods can be crucial to decarbonisation objectives. To appropriately size and design the control of such networks, the modelling of district heating networks should have a good representation of the demand-side, which is the set of buildings connected to the network. In-stead of simplified modelling of the demand, whole-building simulation tools can be invoked in this case, like EnergyPlus. More recently, equation-based libraries have been developed in Mod-elica for component-based simulation of HVAC systems. Modelica-based libraries offer easier model composability and are particularly interesting for control fine-tuning; on the downside, the model setup can be more complex, with more validation needed. This paper conducts a comparative study of the Modelica LBNL Buildings library against Ener-gyPlus, based on an archetype-based hypothetical case in the UK with a small-scale district heating system. The methodology resides on models developed in the two tools with the same level of modelling detail. The comparison helps understand software differences in the model-ling procedure, computational time, relative accuracy of energy predictions and heating system variables. The results indicate Modelica Buildings library yields similar accuracy in terms of heat transfer calculation through thermal zones as EnergyPlus, whilst capturing additional en-ergy consumption caused by the dynamic changes at system startup and the realistic controllers used in the Modelica district heating models. Meanwhile, the Modelica Buildings library’s out-puts show the fluctuations of system variables, indicating different operation patterns and con-trol effects against EnergyPlus. This study also proves that the Modelica Buildings library is the better tool for district heating simulation in the context of dynamic performance evaluation and control testing, based on overall capabilities, limitations, and prediction differences

Data-driven smart buildings: Narratives of drivers and barriers from real-world implementations

Progress in the digitalisation of building services has been slow. Data-driven insights combined with innovative business models have the potential to unlockvalue. Yet barriers associated with implementing smart building technologies in the real worldinclude an unclear value proposition, differing stakeholder perspectives, and limited evidence of the benefits and disbenefits. This paper reports ongoingwork within the International Energy Agency Annex 81, “Data-driven smart buildings, ”to understandthe current technology landscape and opportunities by implementingdata-driven building servicesin non-domestic buildings. Several case studies were collected fromaround the world, contributed by Annex81 participants. This paper discusses stakeholder narratives on the value proposition and lessons learnt from the case studies collected and gives practical suggestions to overcomedigitalisation barriers

Electronic Information Major Practice Teaching Reform Concentrated Research and Innovation

AbstractBringing forward the concentration of practical teaching system reform ideas are based on product-oriented, hierarchical progressive. The idea associates all parts of practice, establishing a large framework system. This approach reflects the intrinsic link between knowledge, reached through the practice of teaching will be the focus of a comprehensive knowledge of the purpose. Its implementation will help to change the traditional teaching mode, and to further promote the theory of teaching, thereby raising the overall quality of training

Research on Axial Mechanical Properties of the Grouted Connection Section Considering Installation Errors

[Introduction] With the development of offshore wind turbine works to deep sea areas, the challenging construction environment tends to result in errors in the installation of the grouted connection for the jacket foundation. These errors can subsequently affect the axial mechanical properties of the grouted connection. Therefore, it is necessary to study the impact laws of installation errors on the axial mechanical properties of the grouted connection section. [Method] The study was commenced by conducting axial static loading tests on reduced-scale test piece of the grouted connection section, which was followed by simulating the axial loading process of the corresponding test piece using the finite element analysis method. The simulation results were found to align well with the experimental data, indicating a successful outcome. [Result] According to the research findings, the increasing in longitudinal and transverse installation errors can lead to an increase in the axial stiffness of the grouted connection section. This, in turn, further alters the longitudinal strain distribution of the casing and pile pipe. Additionally, the increase in installation errors can lead to an increase in the maximum value of the third principal stress in the grouting materials during the axial loading process, as well as changes in its distribution location. [Conclusion] In conclusion, the influence of installation errors on the axial mechanical properties of the grouted connection section for the jacket foundation can cause alterations in failure modes of the grouted connection section. Therefore, it is needed to consider and evaluate the harm caused by the impact laws of installation errors based on their influence rules

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods

Patient-Specific Coronary Artery 3D Printing Based on Intravascular Optical Coherence Tomography and Coronary Angiography

Despite the new ideas were inspired in medical treatment by the rapid advancement of three-dimensional (3D) printing technology, there is still rare research work reported on 3D printing of coronary arteries being documented in the literature. In this work, the application value of 3D printing technology in the treatment of cardiovascular diseases has been explored via comparison study between the 3D printed vascular solid model and the computer aided design (CAD) model. In this paper, a new framework is proposed to achieve a 3D printing vascular model with high simulation. The patient-specific 3D reconstruction of the coronary arteries is performed by the detailed morphological information abstracted from the contour of the vessel lumen. In the process of reconstruction which has 5 steps, the morphological details of the contour view of the vessel lumen are merged along with the curvature and length information provided by the coronary angiography. After comparing with the diameter of the narrow section and the diameter of the normal section in CAD models and 3D printing model, it can be concluded that there is a high correlation between the diameter of vascular stenosis measured in 3D printing models and computer aided design models. The 3D printing model has high-modeling ability and high precision, which can represent the original coronary artery appearance accurately. It can be adapted for prevascularization planning to support doctors in determining the surgical procedures

Prespore Cell Fate Bias in G(1) Phase of the Cell Cycle in Dictyostelium discoideum

By generating a population of Dictyostelium cells that are in the G(1) phase of the cell cycle we have examined the influence of cell cycle status on cell fate specification, cell type proportioning and its regulation, and terminal differentiation. The lack of observable mitosis during the development of these cells and the quantification of their cellular DNA content suggests that they remain in G(1) throughout development. Furthermore, chromosomal DNA synthesis was not detectable these cells, indicating that no synthesis phase had occurred, although substantial mitochondrial DNA synthesis did occur in prespore cells. The G(1)-phase cells underwent normal morphological development and sporulation but displayed an elevated prespore/prestalk ratio of 5.7 compared to the 3.0 (or 3:1) ratio normally observed in populations dominated by G(2)-phase cells. When migrating slugs produced by G(1)-phase cells were bisected, each half could reestablish the 5.7 (or 5.7:1) prespore/prestalk ratio. These results demonstrate that Dictyostelium cells can carry out the entire developmental cycle in the G(1) phase of the cell cycle and that passage from G(2) into G(1) phase is not required for sporulation. Our results also suggest that the population asymmetry provided by the distribution of cells around the cell cycle at the time of starvation is not strictly required for cell type proportioning. Finally, when developed together with G(2)-phase cells, G(1)-phase cells preferentially become prespore cells and exclude G(2)-phase cells from the prespore-spore cell population, suggesting that G(1)-phase cells have an advantage over G(2)-phase cells in executing the spore cell differentiation pathway

Multi-level identification performance for RC-based control-oriented model of the UK office archetype

Resistance-capacitance-based grey-box models are widely adopted as one of the modelling solutions in model-predictive controls. These models have been evaluated to determine the optimal level of complexity in standardised cases. However, further evaluations are needed to draw more universal conclusions across diverse scenarios, modelling approaches, and operational conditions. In this study, a series of grey-box models were identified by MPCPy based on a British office model, followed by a parametric analysis on model format, modelling details, training data volume, and validation periods. The R2C2 model yielded the most accurate predictions with less deviations, and more accurate estimations were observed in multi-zone models. Additionally, it is suggested to consider direct normal irradiance as a modelling input in multi-zone models, and adaptive re-calibrations are recommended when significant changes in solar radiations occur