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Povzetek: Shallow geothermal energy systems (SGES) may take different forms and have recently taken considerable attention due to energy geo-structures (EGS) resulting from the integration of heat exchange elements in geotechnical structures. Still, there is a lack of systematic design guidelines of SGES. Hence, in order to contribute towards that direction, the current study aims at reviewing the available SGES modeling options along with their various aspects and practices. This is done by first presenting the main analytical and numerical models and methods related to the thermal behavior of SGES. Then, the most important supplementary factors affecting such modeling are discussed. These include: (i) the boundary conditions, in the form of temperature variation or heat flow, that majorly affect the predicted thermal behavior of SGES; (ii) the spatial dimensions that may be crucial when relaxing the infinite length assumption for short heat exchangers such as energy piles (EP); (iii) the determination of SGES parameters that may need employing specific techniques to overcome practical difficulties; (iv) a short-term vs. long-term analysis depending on the thermal storage characteristics of GHE of different sizes; (v) the influence of groundwater that can have a moderating effect on fluid temperatures in both heating and cooling modes. Subsequently, thermo-mechanical interactions modeling issues are addressed that may be crucial in EGS that exhibit a dual functioning of heat exchangers and structural elements. Finally, a quite lengthy overview of the main software tools related to thermal and thermo-hydro-mechanical analysis of SGES that may be useful for practical applications is given. A unified software package incorporating all related features of all SGES may be a future aim.
Ključne besede: shalow geothermal energy systems, energy geo-structures, thermal analysis, thermo-hydro-mechanical, modelling, software tools
Objavljeno v DiRROS: 05.03.2024; Ogledov: 144; Prenosov: 65
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Ključne besede: boron, cast iron, solidification, thermal analysis, Thermo-Calc
Objavljeno v DiRROS: 07.02.2024; Ogledov: 162; Prenosov: 74
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Povzetek: This study presents a comprehensive investigation of the formation, composition and behaviour of oxide layers during the high-temperature oxidation of four different steel alloys (16Mo3, 13Cr, T24 and P91) at a uniform temperature of 650 °C. The study is aimed at assessing the oxidation damage due to short-term overheating. The research combines CALPHAD (CALculation of PHAse Diagrams) calculations, thermogravimetric analysis (TGA) and advanced microscopy techniques, in- cluding scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD), to elucidate the complex mechanisms controlling oxidation kinetics and oxide layer development. CALPHAD calculations were used to determine the thermodynamically stable phases for each steel type at 650 ◦C and different oxygen activities. The results showed different phase compositions, highlighting the importance of the chromium content in steel for the formation of oxide layers. The different oxidation kinetics and oxide layer compositions are presented and associated with the increased risk of material degradation due to overheating. These results have significant implications for industrial applications, mainly the susceptibility to oxidation of low-alloyed steels like 16Mo3 and 13 Cr and contribute to a deeper understanding of oxidation processes in steels.
Ključne besede: high-temperature oxidation, thermogravimetric analysis, kinetics, CALPHAD, boiler steels, SEM, EBSD
Objavljeno v DiRROS: 26.01.2024; Ogledov: 238; Prenosov: 113
Celotno besedilo (12,99 MB)
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Povzetek: Introduction: The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents. Materials and methods: In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach. Results: A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles. Conclusion: We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.
Ključne besede: diabetes mellitus, skeletal muscle, metabolism, macromulecular composition, infrared spectroscopy, multivariate analysis, histochemical assays
Objavljeno v DiRROS: 11.01.2024; Ogledov: 265; Prenosov: 96
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Povzetek: The effects of data governance (as a means to maximize big data value creation in fire risk management) performance on fire risk was analyzed based on multi-source statistical data of 105 cities in China from 2016 to 2018. Specifically, data governance was first quantified with ten detailed indicators, which were then selected for explaining urban fire risk through correlation analysis. Next, the sample cities were clustered in terms of major socio-economic characteristics, and then the effects of data governance were examined by constructing multivariate regression models for each city cluster with ordinary least squares (OLS). The results showed that the constructed regression models produced good interpretation of fire risk in different types of cities, with coefficient of determination (R2) in each model exceeding 0.65. Among the indicators, the development of infrastructures (e.g. data collection devices and data analysis platforms), the level of data use, and the updating of fire risk related data were proved to produce significant effects on the reduction of fire frequency and fire consequence. Moreover, the organizational maturity of data governance was proved to be helpful in reducing fire frequency. For the cities with large population, the cross-department sharing of high-value data was found to be another important determinant of urban fire frequency. In comparison with existing statistical models which interpreted fire risk with general social factors (with the highest R2 = 0.60), these new regression models presented a better statistical performance (with the average R2 = 0.72). These findings are expected to provide decision support for the local governments of China and other jurisdictions to facilitate big data projects in improving fire risk management.
Ključne besede: urban fire risk, fire risk management, big data technologies, data governance, socio-economic factors, city-wide analysis
Objavljeno v DiRROS: 09.01.2024; Ogledov: 173; Prenosov: 35
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