Search Results (165)

Using solid particulates as a heat transfer medium for concentrated solar power (CSP) systems has many advantages, positioning them as a superior option compared with conventional heat transfer media such as steam, oil, air, and molten salt. However, a critical imperative lies in the comprehensive evaluation of the properties of potential solid particulates intended for utilization under such extreme thermal conditions. This paper undertakes an exhaustive examination of both ambient and high-temperature thermophysical properties of four naturally occurring particulate materials, Riyadh white sand, Riyadh red sand, Saudi olivine sand, and US olivine sand, and one well-known engineered particulate material. The parameters under scrutiny encompass loose bulk density, tapped bulk density, real density, sintering temperature, and thermal conductivity. The results reveal that the theoretical density decreases with the increase in temperature. The bulk density of solid particulates depends strongly on the particulate size distribution, as well as on the compaction. The tapped bulk density was found to be larger than the loose density for all particulates, as expected. The sintering test proved that Riyadh white sand is sintered at the highest temperature and pressure, 1300 °C and 50 MPa, respectively. US olivine sand was solidified at 800 °C and melted at higher temperatures. This proves that US olivine sand is not suitable to be used as a thermal energy storage and heat transfer medium in high-temperature particle-based CSP systems. The experimental results of thermal diffusivity/conductivity reveal that, for all particulates, both properties decrease with the increase in temperature, and results up to 475.5 °C are reported. Full article

To elucidate the key material parameters governing the tribological performance of ceramic composites under dry sliding against steel, this study presents a comprehensive comparative assessment of the microstructural characteristics, mechanical performance, and tribological behavior of two alumina–zirconia (Al₂O₃–ZrO₂) ceramic composites, each reinforced with a 42 vol.% carbide phase: zirconium carbide (ZrC) and tungsten carbide (WC). Specifically, tungsten carbide (WC) was selected for its exceptional bulk mechanical properties, while zirconium carbide (ZrC) was chosen to contrast its potentially different interfacial reactivity against a steel counterface. ZrC and WC were selected as reinforcing phases due to their high hardness and distinct chemical and interfacial properties, which were expected to critically affect the wear and friction behavior of the composites under demanding conditions. Specimens were consolidated via spark plasma sintering (SPS). The investigation encompassed macro- and nanoscale hardness measurements (Vickers hardness HV₁, HV₁₀; nanoindentation hardness H), elastic modulus (E), fracture toughness (K_IC), coefficient of friction (COF), and specific wear rate (W_s) under unlubricated reciprocating sliding against 100Cr6 steel at normal loads of 10 N and 25 N. The Al₂O₃–ZrO₂–WC composite exhibited an ultrafine-grained microstructure and markedly enhanced mechanical properties (HV₁₀ ≈ 20.9 GPa; H ≈ 33.6 GPa; K_IC ≈ 4.7 MPa·m^½) relative to the coarse-grained Al₂O₃–ZrO₂–ZrC counterpart (HV₁₀ ≈ 16.6 GPa; H ≈ 27.0 GPa; K_IC ≈ 3.2 MPa·m^½). Paradoxically, the ZrC-reinforced composite demonstrated superior tribological performance, with a low and load-independent specific wear rate (W_s ≈ 1.2 × 10⁻⁹ mm³/Nm) and a stable steady-state COF of approximately 0.46. Conversely, the WC-reinforced system exhibited significantly elevated wear volumes—particularly under the 25 N regime—and a higher, more fluctuating COF. Scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM–EDX) of the wear tracks revealed the formation of a continuous, iron-enriched tribofilm on the ZrC composite, derived from counterface material transfer, whereas the WC composite surface displayed only sparse tribofilm development. These findings underscore that, in steel-paired tribological applications of Al₂O₃–ZrO₂–based composites, the efficacy of interfacial tribolayer generation can supersede intrinsic bulk mechanical attributes as the dominant factor governing wear resistance. Full article

Robust dynamic equivalents of large power networks are essential for fast and reliable stability analysis of bulk power systems. This is because the dimensionality of modern power systems raises convergence issues in modern stability-analysis programs. However, even with modern computational power, it is challenging to find reduced-order models for power systems due to the following factors: the tedious mathematical analysis involved in the classical reduction techniques requires large amounts of computational power; inadequate information sharing between geographical areas prohibits the execution of model-dependent reduction techniques; and frequent fluctuations in the operating conditions (OPs) of power systems necessitate updates to reduced models. This paper focuses on a measurement-based approach that uses a deep artificial neural network (DNN) to estimate the dynamics of an external system (ES) of a power system, enabling stability analysis of a study system (SS). This DNN technique requires boundary measurements only between the SS and the ES. However, machine learning-based techniques like this DNN are known for their extensive training requirements. In particular, for power systems that undergo continuous fluctuations in operating conditions due to the use of renewable energy sources, the applications of this DNN technique are limited. To address this issue, a Deep Transfer Learning (DTL)-based technique is proposed in this paper. This approach accounts for variations in the OPs such as time-to-time variations in loads and intermittent power generation from wind and solar energy sources. The proposed technique adjusts the parameters of a pretrained DNN model to a new OP, leveraging symmetry in the balanced adaptation of model layers to maintain consistent dynamics across operating conditions. The experimental results were obtained by representing the Queensland (QLD) system in the simplified Australian 14 generator (AU14G) model as the SS and the rest of AU14G as the ES in five scenarios that represent changes to the OP caused by variations in loads and power generation. Full article

In this study, we propose a self-adaptive weighted multi-mineral inversion model (SQP_AW) based on Sequential Quadratic Programming (SQP) and the Adam optimization algorithm for the accurate evaluation of mineral content in carbonate reservoir rocks, addressing the high costs of traditional experimental methods and the strong parameter dependence in geophysical inversion. The model integrates porosity curves (compensated density, compensated neutron, and acoustic time difference), elastic modulus parameters (shear and bulk moduli), and nuclear magnetic porosity data for the construction of a multi-dimensional linear equation system, with calibration coefficients derived from core X-ray diffraction (XRD) data. The Adam algorithm dynamically optimizes the weights, solving the overdetermined equation system. We applied the method to the Asmari Formation in the M oilfield in the Middle East with 40 core samples for calibration, achieving a 0.91 fit with the XRD data. For eight additional uncalibrated samples from Well A, the fit reaches 0.87. With the introduction of the elastic modulus and nuclear magnetic porosity, the average relative error in mineral content decreases from 9.45% to 6.59%, and that in porosity estimation decreases from 8.1% to 7.1%. The approach is also scalable to elemental logging data, yielding inversion precision comparable to that of commercial software. Although the method requires a complete set of logging data and further validation of regional applicability for weight parameters, in future research, transfer learning and missing curve prediction could be incorporated to enhance its practical utility. Full article

Temperature polarization is a critical factor influencing the performance of membrane distillation. The presence of temperature polarization causes the temperature of the fluid near the membrane surface to be different from that in the bulk region, reducing the effective temperature difference across the membrane and thus diminishing the transmembrane mass transfer driving force. This study investigates the monitoring of temperature polarization and its effects on the transmembrane mass transfer performance in a typical air gap membrane distillation system. A set of thermocouples within a feed module were employed to monitor and capture the development of the temperature polarization profile. The test results reveal that temperature polarization reduces the effective temperature difference across the membrane, leading to a certain difference between the theoretical estimation and experimental values of the mass transfer coefficient across the porous membrane. To address this issue, the temperature polarization factor was further analyzed as a metric to quantify the impact of temperature polarization on the transmembrane flux in membrane distillation, with a detailed discussion of its range and implications. Full article

Accurate Total Organic Carbon (TOC) prediction in the deeply buried Lower Cambrian Qiongzhusi Formation shale is constrained by extreme heterogeneity (TOC variability: 0.5–12 wt.%, mineral composition Coefficient of Variation > 40%) and ambiguous geophysical responses. This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R² = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R² = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R² = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). The methodology establishes a transferable framework for organic-rich shale evaluation, directly applicable to the Longmaxi Formation and global Precambrian–Cambrian transition sequences. Future directions emphasize real-time drilling data integration and quantum computing-enhanced modeling for ultra-deep shale systems, advancing predictive capabilities in tectonically complex basins. Full article

Long-term human residence on the Moon is an inevitable trend in lunar exploration, necessitating the development of Bioregenerative Life Support Systems (BLSSs). In BLSSs, plant cultivation serves as the core functional unit, requiring substantial amounts of cultivation substrates. Lunar soil has potential as a cultivation substrate, but its suitability for plant growth must be improved to meet life-support requirements. As a fine-grained, organics-free, in situ resource, lunar soil’s high compaction significantly restricts crops’ root access to oxygen, water, and nutrients. While the addition of organic solid waste—a byproduct of BLSSs—could alleviate compaction, issues such as salinization, incomplete decomposition, and the presence of pathogens pose risks to crop health. In this study, we introduced earthworms into wheat cultivation systems to gradually digest, transfer (as vermicompost), and mix solid waste with a lunar soil simulant substrate. We set five experimental groups: a positive control group using vermiculite (named as V) as the optimal growth substrate, a negative control group using pure lunar soil simulant (LS), and three treatment groups using lunar soil simulant with solid waste and 15 (LS+15ew), 30 (LS+30ew), and 45 (LS+45ew) earthworms added. Our results demonstrated significant improvements in both compaction (e.g., bulk density, hydraulic conductivity) and salinization (e.g., salinity, electrical conductivity), likely due to the improved soil aggregate structures, which increased the porosity and ion adsorption capacity of the soil. Additionally, the microbial community within the substrate shifted toward a cooperative pattern dominated by significantly enriched plant probiotics. Consequently, the cultivated wheat achieved approximately 80% of the growth parameters (including production) compared to the control group grown in vermiculite with nutrient solution (representing ideal cultivation conditions), indicating sufficient nutrient supply from the mineralized waste. We can conclude that the earthworms “complementarily” improved the lunar soil simulant and organic waste by addressing compaction and salinization, respectively, leading to comprehensive improvements in key parameters, including the microbial environment. This study proposes a conceptual framework for improving lunar soil for crop cultivation, and it innovatively introduces earthworms as a preliminary yet effective solution. These findings provide a feasible and inspiring foundation for future lunar agriculture. Full article

Mullite–corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, thermal stability, and chemical resistance. Establishing relationships and mechanisms through traditional experiments is time-consuming and labor-intensive. In this study, gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN) models were developed to predict essential properties such as apparent porosity, bulk density, water absorption, and flexural strength of mullite–corundum ceramics. The GBR model (R² 0.91–0.95) outperformed the RF and ANN models (R² 0.83–0.89 and 0.88–0.91, respectively) in accuracy. Feature importance and partial dependence analyses revealed that sintering temperature and K₂O (~0.25%) positively affected bulk density while negatively influencing apparent porosity and water absorption. Additionally, sintering temperature, additives, and Fe₂O₃ (optimal content ~5% and 1%, respectively) were positively related to flexural strength. This approach provided new insight into the relationships between feedstock compositions and sintering process parameters and ceramic properties, and it explored the possible mechanisms involved. Full article

High power density and better efficiency make the multiterminal high-voltage direct current (MT-HVDC) system the best candidate for long-distance bulk power transfer in the cases of onshore and offshore power systems. Many machine learning-based algorithms have been developed for the protection of MT-HVDC systems. However, the exploration of the effects of change in the fault and ground resistances of MT-HVDC systems has not been studied comprehensively. In this study, a four-terminal HVDC test system is employed for the analysis of the effects on fault diagnosis under change in the fault and ground resistances. A simplified medium tree-based machine learning algorithm that works on Gini’s index of diversity is developed for fault diagnosis in the MT-HVDC system. It is found from the simulation analysis that the preprocessing based on mean and differences in featured data extracted for fault current is required to reduce the impacts of the accuracy of machine learning algorithms. The preprocessing not only retains the accuracy of the machine learning algorithm in different cases of faults, but also minimizes the reduction in accuracy in some fault cases. In the test cases, the accuracy is 88.7%, 60%, and 57.1% without preprocessing of featured data for the machine learning algorithm under different values of fault and ground resistances, but the accuracy is improved to 99.5%, 84.1%, and 77.8%, respectively. Hence, the machine learning algorithm can be made applicable under different values of fault and ground resistances for the protection of the MT-HVDC system. This helps to develop a protected MT-HVDC system for long distances without the fear of different soil conditions. Full article

Hydrogen-based fuel-cell systems are a promising technology for reducing carbon footprint in the portfolio of future propulsion system concepts for small-range and regional aircraft In order to increase efficiency, the application of a turbo-charged air supply, using a compressor stage, a turbine stage, and an electric motor, has proven to be beneficial. This paper explores the thermal management aspects of a pioneering Electrified Turbo Charger designed for fuel-cell applications. A novel approach employing gas-cooling for the electric machine is investigated through simulation using an adiabatic Computational Fluid Dynamics (CFD) model. Bulk-flow-based Heat Transfer Coefficients (BHTCs) and temperatures are extracted from the CFD Analysis and serve as boundary conditions in a Solid Thermal model. Additionally, a 3D transient electromagnetic analysis is employed to assess losses in various components of the machine, which are then integrated into the 3D Solid Thermal Model. Initial evaluation of the temperature distribution is conducted, and subsequent analysis highlights uncertainties inherent in this methodology. Full article

Historically, a large fraction of fatigue testing of both components and structures has been performed using hydraulic actuators. These are typically driven by servo-valves, which are in themselves very inefficient. But, as most tests involve elastically stressing mechanical components, a lot of stored energy could be recovered. Unfortunately, servo-valves are not regenerative—simply metering out fluid in order to relax the system prior to the start of the next cycle. There is much to be gained with a more intelligently controlled system. The FastBlade facility in Scotland uses a new type of regenerative test hydraulics. Digital displacement pump/motors (DDPMs), originated by Artemis Intelligent Power, now Danfoss Scotland, are used to load and unload the test structure directly via hydraulic rams. The DDPMs are driven by induction motors supplied by three-phase frequency converters, each with a very loose speed correction target, such that they can speed up or slow down according to the instantaneous torque exerted by the load. The rotating assembly of the induction motor and DDPM is designed to have sufficient inertia so as to function as a kinetic energy storage flywheel. The loading energy is then cyclically transferred between the rotating inertia of the motor/DDPM and the spring energy in the test structure. The electric motor provides sufficient energy to maintain the target average cyclical shaft speed of the DDPM whilst the bulk of the system energy oscillates between the two storage mechanisms. Initial tests (at low load) suggest that this technique requires only 30% of the energy previously needed. FastBlade is a unique facility built by the University of Edinburgh and Babcock, with support from the UK EPSRC, conceived as a means of testing and certifying turbine blades for marine current turbines. However, this approach can be used in any cyclical application where elastic energy is stored. Full article

Transfer chutes are crucial components in handling bulk materials using belt conveyors. The flow of material through these devices is influenced by several variables. Traditionally, these devices have been designed based on prior experience. However, the increase in computational capacity has enabled the application of the Discrete Element Method (DEM) in their simulation by modeling the behavior of individual particles forming the bulk. The greatest challenge in this process is the selection of appropriate contact models and parameters that accurately reflect the material response. The work describes an approach used to calibrate contact parameters of a sample of moist iron ore while interacting with two distinct surface materials. The methodology starts with a variety of bench-scale tests, followed by experiments in a pilot-scale handling system, concluding with a semi-quantitative verification in an industrial-scale chute using three commercial DEM software. The findings indicate that certain tests are more responsive to specific material behavior, so that their combined use allows for a realistic representation of the material flow, even when using virtual spherical particles. Full article

Kernel breakage and fungi-induced hot spots can easily lead to potential safety hazards in maize storage. The objective of this study was to focus on the formation and development of hot spots in maize bulk with two different broken kernels contents (BKCs), i.e., 4.26% (BKC_4.26) and 6.14% (BKC_6.14), and a moisture content of 16.3% under the same storage conditions. A multifunctional simulation system was developed to simulate the heat and moisture transfer process in stored grain bulk, and a new method was proposed to evaluate the effect of local hot spots on the storage safety of maize bulk with different BKCs. The results showed that there are differences in fungal respiration rates in the maize bulk with two different BKCs, and the temperature impact range caused by hot spots under the same storage conditions was different. The maximum temperature caused by fungal growth in BKC_4.26 and BKC_6.14 was 37.47 °C and 38.81 °C, and the proportion of high-temperature areas caused was 64.2% and 62.3%. The relative humidity at local hot spots continued to decrease, reaching 64.8% and 71.7% when stored for 1800 h in BKC_4.26 and BKC_6.14. The CO₂ concentration at hot spots in BKC_6.14 was higher than that of BKC_4.26, while the O₂ concentration was lower than BKC_4.26. Dry matter loss (DML) at the hot spots in BKC_6.14 was higher than that in BKC_4.26. A nonlinear model was developed to predict temperature changes of fungi-induced hot spots in maize bulk considering the storage time, temperature, relative humidity, and CO₂ concentration at the hot spots, and the model fit the experimental data reasonably well. Full article

The objectives of this work were as follows: (i) the evaluation of the prevalence of detection of genetic material of Coxiella burnetii in the bulk tank milk of sheep and goat farms in Greece and (ii) the investigation of variables related to the management applied in farms as possible predictors for this. The presence of C. burnetii genetic material was studied in the bulk tank milk of 325 sheep and 119 goat farms throughout the country. For qualitative and quantitative identification of the genetic material of the pathogen, a commercially available real-time PCR was used. In total, 45 parameters were assessed for potential association with the detection of the pathogen: these referred to the management system, infrastructure, health management, animals, production characteristics, and human resources on the farms. Genetic material of the pathogen was detected in bulk tank milk samples from nine sheep (2.8%) and six goat (5.0%) farms. Genetic material was at significantly higher median concentrations in samples from goat farms than from sheep farms, 1,078,096 (min: 181,121, max: 2,331,386) versus 15,728 (min: 507, max: 505,852) GE mL⁻¹, respectively. For sheep farms, the intensive or semi-intensive management system applied in farms (p = 0.003), and for goat farms, the intensive or semi-intensive management system applied in farms (p = 0.0007) and the smaller number of annual veterinary visits to farms (p = 0.044) emerged as significant predictors. Among sheep farms managed under the intensive or semi-intensive system, the lack of accessory barns on farms (p = 0.024) emerged as a significant predictor; no significant predictor could be found among goat farms under such management systems. There was no significant difference in production outcomes between farms in which C. burnetii was or was not detected in the bulk tank milk; also, there was no association between the detection of C. burnetii and the annual incidence rate of cases of abortion on the farms. The results suggest that the risk of transfer of C. burnetii to dairy products from sheep and goat milk appears to be small, but not negligible, which indicates that the pasteurization of milk from small ruminants must be carried out consistently and correctly to ensure the safety of the product. Full article

Contact electrification (CE) spans from atomic to macroscopic scales, facilitating charge transfer between materials upon contact. This interfacial charge exchange, occurring in solid–solid (S–S) or solid–liquid (S–L) systems, initiates radical generation and chemical reactions, collectively termed contact-electro-chemistry (CE-Chemistry). As an emerging platform for green chemistry, CE-Chemistry facilitates redox, luminescent, synthetic, and catalytic reactions without the need for external power sources as in traditional electrochemistry with noble metal catalysts, significantly reducing energy consumption and environmental impact. Despite its broad applicability, the mechanistic understanding of CE-Chemistry remains incomplete. In S–S systems, CE-Chemistry is primarily driven by surface charges, whether electrons, ions, or radicals, on charged solid interfaces. However, a comprehensive theoretical framework is yet to be established. While S–S CE offers a promising platform for exploring the interplay between chemical reactions and triboelectric charge via surface charge modulation, it faces significant challenges in achieving scalability and optimizing chemical efficiency. In contrast, S–L CE-Chemistry focuses on interfacial electron transfer as a critical step in radical generation and subsequent reactions. This approach is notably versatile, enabling bulk-phase reactions in solutions and offering the flexibility to choose various solvents and/or dielectrics to optimize reaction pathways, such as the degradation of organic pollutants and polymerization, etc. The formation of an interfacial electrical double layer (EDL), driven by surface ion adsorption following electron transfer, plays a pivotal role in CE-Chemical processes within aqueous S–L systems. However, the EDL can exert a screening effect on further electron transfer, thereby inhibiting reaction progress. A comprehensive understanding and optimization of charge transfer mechanisms are pivotal for elucidating reaction pathways and enabling precise control over CE-Chemical processes. As the foundation of CE-Chemistry, charge transfer underpins the development of energy-efficient and environmentally sustainable methodologies, holding transformative potential for advancing green innovation. This review consolidates recent advancements, systematically classifying progress based on interfacial configurations in S–S and S–L systems and the underlying charge transfer dynamics. To unlock the full potential of CE-Chemistry, future research should prioritize the strategic tuning of material electronegativity, the engineering of sophisticated surface architectures, and the enhancement of charge transport mechanisms, paving the way for sustainable chemical innovations. Full article