Search Results (92)

Hyperloop transport operates in a low-pressure environment in which convective heat transfer is strongly limited, making conventional air-based cooling ineffective. One promising thermal management approach is therefore to absorb the waste heat generated during travel in a thermal energy storage (TES) system and dissipate it during stops. In this context, latent heat storage based on water–ice systems is particularly attractive because of its high energy density and nearly constant-temperature heat absorption. However, experimental validation of such systems beyond laboratory scale is still lacking. This study therefore investigated a large-scale direct contact latent heat storage (DCLHS) system for Hyperloop thermal management, using water as heat transfer fluid and ice as phase change material. The system was evaluated for two ice morphologies, crushed ice and ice block, under both constant and time-variant cooling power profiles representative of Hyperloop operation. The objective was to assess thermal performance, exergy efficiency, and hydraulic stability at application-relevant scale, and to identify morphology-dependent trade-offs relevant for system integration. The results show that the large-scale system can operate reliably under dynamic loads and that upscaling leads to smoother thermal behavior and reduced boundary effects. Crushed ice demonstrated superior thermal responsiveness, maintaining outlet temperatures close to the phase change temperature and achieving exergy efficiencies up to 0.72 at cooling powers up to 3.8 kW while enabling stable operation at 15 °C. In contrast, the ice block configuration provided higher volumetric energy density but exhibited delayed thermal response and required substantially higher mass flow rates, which limited operation to approximately 25 °C and reduced exergy efficiency to 0.03–0.35. Overall, the results show that large-scale DCLHS is a feasible option for Hyperloop thermal management, while also revealing that system behavior at larger scale is strongly influenced by storage morphology. Full article

Background: A lean crystal engineering study was performed on the early pre-clinical POLθ inhibitor MSC178 to enable sufficient exposure for high-dose PK studies. Methods: COSMOquick-derived excess enthalpies in combination with a toxicological assessment of co-formers were used for the selection of four co-formers. Experimental crystallization trials were performed in a staged approach from a 15 mg scale, over a 50 mg upscale, to a final g-scale upscale of the most promising co-crystal form with 2,4-DHBA. Results: The 2,4-DHBA co-crystal form revealed more enhanced and sustained supersaturation plateaus in FaSSIF compared to the amorphous free base form, the 3,4-DHBA co-crystal form, and the 1,2-EDSA salt form. Moreover, the 2,4-DHBA co-crystal form was shown to be physically stable in the suspension vehicle for the PK study. The high physical stability toward physical-form conversion in the suspension vehicle as well as the more sustained supersaturation plateau in the non-sink dissolution profile could be attributed to the intrinsic features of the crystal structure as well as the assessed surface hydrophilicity of the co-crystal particles, both suggesting that rather hydrophobic surfaces are present that help preferentially attract stabilizing surfactants from the dissolution medium (taurocholate) and from the suspension vehicle (polysorbate, methocel), respectively. Successful upscale of the 2,4-DHBA co-crystal form was achieved in the small g-scale, revealing mainly isotropic crystal growth in primary particles as well as a pronounced tendency toward isotropically shaped dendrite-like secondary particles, both favored by a multi-dimensional hydrogen bonding network being present. Excellent agreement was shown for the extent of in vitro supersaturation behavior and in vivo exposure gain in the high-dose PK study for the 2,4-DHBA co-crystal form versus the amorphous free form. Conclusions: The co-crystal strategy can be successfully developed in early pre-clinical industrial research with lean methodologies to optimize sub-optimal phys.-chem. properties of a free base compound to achieve improved and less variable in vivo exposure between animals in high-dose PK studies. Full article

The macroscopic resistance of asphalt mixtures to permanent deformation is fundamentally governed by the mechanical properties of the constituent asphalt mortar; however, a unified evaluation system that quantitatively links the energy evolution between these two scales is currently lacking. This study aims to bridge this gap by establishing a multiscale framework to characterize and predict the recoverable and dissipated energy behaviors of asphalt materials. To achieve this, Multi-Stress Creep Recovery (MSCR) tests and Multi-Sequence Repeated Loading (MSRL) tests were conducted on asphalt mortar and mixtures, respectively, to capture energy evolution under varying stress, temperature, and gradation conditions. Subsequently, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were developed to correlate mesoscopic mortar parameters with macroscopic mixture performance. Experimental results reveal that energy indicators are significantly influenced by loading stress and aggregate skeleton, with finer gradations exhibiting greater responsiveness to stress changes. A strong cross-scale dependency was identified, evidenced by a correlation coefficient of 0.86 between the recoverable energy of the mixture ($U_{r-mix}$) and that of the mortar ($U_{r-mortar}$). Furthermore, the developed ANN model demonstrated exceptional predictive accuracy ($R^2\ge 0.99$) in upscaling energy indicators. This study develops a multiscale energy framework that integrates experimentally derived energy indicators from asphalt mortar and asphalt mixture, enabling the prediction of macroscopic mixture performance from mesoscopic mortar energy evolution rather than relying solely on empirical machine-learning correlations. Full article

To address the challenges of extracting mechanical fault features in Gas Insulated Switchgear (GIS) under complex operating conditions and the insufficient diagnostic accuracy associated with traditional one-dimensional time-series signals, this paper proposes a GIS fault-classification method based on composite dimensional upscaling images of vibration signals and the Vision Transformer (ViT) algorithm. This method first employs a sliding window slicing strategy to segment the raw long-sequence vibration signals into multiple overlapping time segments. Then, it utilizes the Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), and Markov Transition Field (MTF) to perform composite dimensional upscaling on these segmented signals, projecting the resulting features into a three-channel RGB composite two-dimensional image. Subsequently, the global self-attention mechanism of the Vision Transformer (ViT) processes the dimensionally upscaled data to achieve the fault classification of the GIS equipment. Experimental results demonstrate that, compared to single-channel ViT variants, Convolutional Neural Networks (CNN), and Residual Networks (ResNet), the proposed algorithm achieves the highest overall performance in the training set experiments, and the superiority of this method is verified through ablation studies and comparative experiments. Furthermore, the average accuracy of the algorithm on the testing set reaches 95.63%, proving the reliability and accuracy of the proposed method. Full article

An assessment and prediction system for the quality of public water networks was developed, using Timișoara, Romania, as a case study. This was implemented on a Google Firebase cloud storage system and comprised twelve ML algorithms applied to test samples for drinkability and used in predictions of upcoming samples. The system compares 17 water quality parameters to the World Health Organization and public reports of Timișoara drinking water standards for 804 samples. The system provides real-time data storage, drinkability prediction for the reservoir water system, and rule-based critical water recommendations for elementary treatment in samples. The most accurate and best-calibrated against random forest, gradient boosting, and Logistic Regression algorithms was the decision tree algorithm of the ML models. The experimental findings also determine the regions of the worst and best water quality and propose respective treatment. In contrast to previous research and structures, the paper demonstrates an approved stable solution for smart water monitoring, correlating practical deployment with sophisticated data-based conclusions. The results contribute to enhancing public health, enhancing water management measures, and upscaling the system for larger-scale applications. Full article

Urban point source pollution has been effectively controlled in recent years; however, rainfall-driven non-point source (NPS) pollution has become a major contributor to the deterioration of urban water environments. This study focuses on the plain river network region of Wuxi City in the Taihu Basin, China. By integrating field monitoring with coupled model simulations, this study upscaled results from the experimental plot to the urban-scale river network, enabling analysis of the full processes of pollutant inflow and transport and evaluation of the role of rainfall in regulating these dynamics. Field monitoring in the experimental plot demonstrated a strong correlation between the temporal dynamics of NPS pollutant inflows and rainfall characteristics. Further analysis using model simulations in the river network area revealed that rainfall, maximum 1 h rainfall, and rainfall duration were identified as the primary drivers of pollutant inflows, while early drought duration, rainfall intensity, and variance between rainfall per unit time exerted non-linear effects. Specifically, when early drought duration was approximately 6–7 days or when rainfall intensity ranged from 2.1 to 2.6 mm/h, riverine nitrogen (N) and phosphorus (P) concentrations and pollutant loadings reached their peaks. In addition, when the deviation of unit-time rainfall from the event mean was between 1.8 and 2 mm, the duration of increase in pollutants entering the river was the longest. This study provides quantitative evidence highlighting the influence of rainfall characteristics on nitrogen and phosphorus dynamics in plain river network urban rivers. The findings offer valuable insights into the remediation of urban black-odor water bodies. Full article

Fiber-reinforced polymer composites (FRPCs) are increasingly used in construction due to their high performance and low environmental footprint. However, their widespread adoption has raised concerns over end-of-life management, particularly under European regulations mandating high recycling rates for construction and demolition waste (CDW). This study evaluates different systems for the chemical recycling of FRPCs through microwave (MW)-assisted solvolysis using green solvents, including deep eutectic solvents (DESs) and biobased acetic acid. The process targets thermoset resin depolymerization while preserving fiber integrity, operating at reduced temperatures (≤230 °C) and lower energy demand than conventional techniques, such as pyrolysis. A systematic experimental design was applied to CDW-derived polyester composites and extended to industrial epoxy and vinyl ester composites. Among the tested solvents, glacial acetic acid + ZnCl₂ (5 wt.%), achieved the highest degradation efficiency, exceeding 94% in small-scale trials and maintaining over 78% upon upscaling. Recovered fibers showed moderate property retention, with tensile strength and elongation losses of ~30% and ~45% for infusion-based epoxy composites, while those from pultrusion-based epoxy composites exhibited 16–19% and retained similar properties to the virgin material, respectively. The method facilitates fiber recovery with limited degradation and aligns with circular economy principles through solvent reuse and minimizing environmental impact. Full article

This study addresses a key limitation in meso-scale discrete element modeling (DEM) of ultra-high performance concrete (UHPC). Most existing DEM frameworks rely on extensive macroscopic calibration and do not provide a clear, transferable pathway to derive contact law parameters from measurable micro-scale properties, limiting reproducibility and physical interpretability. To bridge this gap, we develop and validate a micro-indentation-informed, poromechanics-consistent calibration framework that links UHPC phase-level micromechanical measurements to a flat-joint DEM contact model for predicting uniaxial compression, direct tension, and flexural response. Elastic moduli and Poisson’s ratios of the constituent phases are obtained from micro-indentation and homogenization relations, while cohesion (c) and friction angle (α) are inferred through a statistical treatment of the indentation modulus and hardness distributions. The tensile strength limit (σₜ) is identified by matching the simulated flexural stress–strain peak and post-peak trends using a parametric set of (c, α, σₜ) combinations. The resulting DEM model reproduces the measured UHPC responses with strong agreement, capturing (i) compressive stress–strain response, (ii) flexural stress–strain response, and (iii) tensile stress–strain response, while also recovering the experimentally observed failure modes and damage localization patterns. These results demonstrate that physically grounded micro-scale measurements can be systematically upscaled to meso-scale DEM parameters, providing a more efficient and interpretable route for simulating UHPC and other porous cementitious composites from indentation-based inputs. Full article

The Triassic Chang 6 reservoir in the Suijing Oilfield is characterized by poor reservoir quality, pronounced heterogeneity, well-developed fractures, and suboptimal well pattern configuration, which collectively impede the establishment of an efficient displacement system. During the initial development phase, low production rates and delayed lateral response were observed, prompting a tight-spacing infill drilling pilot in the central low-productivity zone. However, conventional fracturing with upscaled stimulation volumes yielded limited fluid production uplift, rapid water cut escalation, and marginal incremental oil recovery. To address these challenges, a dual strategy integrating legacy fracture modification and new fracture generation was developed. Key fracturing parameters influencing reservoir drainage efficiency were systematically investigated, and an orthogonal experimental design was employed to optimize these parameters. The following conclusions were drawn: Stimulation timing should be postponed until water cut stabilizes below 20% in high-productivity zones; the optimal fracture half-length was determined to be 190 m; post-fracturing conductivity was optimized to 30 μm²·cm; and the turning angle for corner wells was set at 23°. For low-productivity zones with impaired reservoir properties that lead to retarded waterfront advancement, refracturing is recommended to be deferred until the water cut reaches 20–40%. The findings of this study provide a theoretical foundation for optimizing on-site refracturing processes and offer valuable guidance for addressing the optimization of fracturing parameters in low-permeability tight sandstone reservoirs. Full article

Sinter-based additive manufacturing (SBAM) processes, such as Cold Metal Fusion (CMF), combine the geometric freedom of additive manufacturing with the scalability of powder metallurgy, but part distortion and collapse during debinding and sintering remain critical design challenges. This study presents a revised stress-based optimization framework to address these issues by integrating sintering-specific load cases into topology optimization. In contrast to earlier approaches, the revised workflow applies all load cases to the upscaled green-part geometry. This adjustment mitigates the non-linear scaling effects of dead load-induced stresses. A Case study, including a steering bracket for a Formula Student racing car, demonstrates that the revised method improves not only sinterability but also application-related performance compared to earlier approaches. In addition, a semi-automated procedure for generating sinter supports is introduced, allowing stable processing of geometries without planar bearing surfaces. Experimental validation confirms that optimized supports effectively prevent part failure during post-processing, though challenges remain in separating complex freeform geometries. Finally, the influence of stiffness on sintering-induced deformations is investigated, showing that higher stiffness configurations significantly reduce dimensional errors. Together, these results highlight stress- and stiffness-based optimization as tools to enhance the reliability, efficiency, and design freedom of SBAM. Full article

Land surface temperature (LST) is a critical parameter in agricultural drought monitoring, crop growth analysis, and climate change research. However, the challenge of acquiring high-resolution LST data with both fine spatial and temporal scales remains a significant obstacle in remote sensing applications. Despite the high temporal resolution afforded by daily MODIS LST observations, the coarse (1 km) spatial scale of these data restricts their applicability for studies demanding finer spatial resolution. To address this challenge, a novel deep learning-based approach is proposed for LST downscaling: the spatial resolution downscaling attention network (SRDA-Net). The model is designed to upscale the resolution of MODIS LST from 1000 m to 250 m, overcoming the shortcomings of traditional interpolation techniques in reconstructing spatial details, as well as reducing the reliance on linear models and multi-source high-temporal LST data typical of conventional fusion approaches. SRDA-Net captures the feature interaction between MODIS LST and auxiliary data through global resolution attention to address spatial heterogeneity. It further enhances the feature representation ability under heterogeneous surface conditions by optimizing multi-source features to handle heterogeneous data. Additionally, it strengthens the model of spatial dependency relationships through a multi-level feature refinement module. Moreover, this study constructs a composite loss function system that integrates physical mechanisms and data characteristics, ensuring the improvement of reconstruction details while maintaining numerical accuracy and model interpret-ability through a triple collaborative constraint mechanism. Experimental results show that the proposed model performs excellently in the simulation experiment (from 2000 m to 1000 m), with an MAE of 0.928 K and an R² of 0.95. In farmland areas, the model performs particularly well (MAE = 0.615 K, R² = 0.96, RMSE = 0.823 K), effectively supporting irrigation scheduling and crop health monitoring. It also maintains good vegetation heterogeneity expression ability in grassland areas, making it suitable for drought monitoring tasks. In the target downscaling experiment (from 1000 m to 500 m and 250 m), the model achieved an RMSE of 1.804 K, an MAE of 1.587 K, and an R² of 0.915, confirming its stable generalization ability across multiple scales. This study supports agricultural drought warning and precise irrigation and provides data support for interdisciplinary applications such as climate change research and ecological monitoring, while offering a new approach to generating high spatio-temporal resolution LST. Full article

Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies. Full article

Fuel electro-hydraulic servo valves are core components in the fuel control system of aero-engines, and their performance directly affects thrust regulation and power output precision. Due to the combustibility of the working medium in fuel systems and the lack of effective circulation filtration, the retention of micron-sized particles within the valve gap can lead to valve spool jamming, which is a critical reliability issue. This study, based on fractal theory and the liquid–solid two-phase flow model, proposes a parametric model for non-ideal surface valve gaps and analyzes the dynamics of particles subjected to drag, lift, and buoyant forces on rough surfaces. By numerically analyzing flow field models with different roughness levels and comparing them with an ideal smooth gap model, the migration characteristics of particles were studied. To verify the accuracy of the model, an upscaled experimental setup was built based on similarity theory, and PIV experiments were conducted for validation. Experimental results show that the particle release position and valve surface roughness significantly affect particle migration time. The weight of the release position on particle migration time is 63%, while the impact of valve surface roughness is 37%. In models with different roughness levels, the particle migration time increases more rapidly for roughness values greater than Ra0.4, while for values less than Ra0.4, the increase in migration time is slower. Furthermore, the study reveals that particle migration trajectories are independent of flow velocity, with velocity only affecting particle migration time. This research provides theoretical support for enhancing the reliability of fuel electro-hydraulic servo valves and offers a new perspective for the design of highly reliable hydraulic components. Full article

In this study, the integration of microwave-assisted technology into fixed-bed configuration processes is explored aiming to characterize and address its challenges with a customized multimodal microwave cavity. This research focuses on evaluating the uncertainty in contactless temperature measurement methods as spectral thermographic cameras and infrared pyrometers, microwave heating performance, and the thermal homogeneity within fixed beds containing microwave–susceptor materials, including the temperature-dependent dielectric characterization of such materials, having different geometry and size (from 120 to 5000 microns). The thermal inhomogeneities along different bed configurations were quantified, assessing the most appropriate fixed-bed arrangement and size limitation at the employed irradiation frequency (2.45 GHz) to tackle microwave-assisted gas–solid chemical conversions. An increased temperature heterogeneity along the axial profile was found for finer susceptor particles, while the higher microwave susceptibility of coarser grades led to increased temperature gradients, ΔT > 300 °C. Moreover, results evidenced that the temperature measurement on the fixed-bed quartz reactor surface by a punctual infrared pyrometer entails a major error regarding the real temperature on the microwave susceptor surface within the tubular quartz reactor (up to 230% deviation). The experimental findings pave the way to assess the characteristics that microwave susceptors and fixed beds must perform to minimize thermal inhomogeneities and optimize the microwave-assisted coupling with solid–gas-phase reactor design and process upscaling using such multimode cavities. Full article

Torsional vibrations pose a serious challenge in drilling operations and can lead to effects such as stick-slip phenomena, tool wear, and reduced drilling efficiency. While previous research has been conducted on torsional vibrations, there is a notable gap in comparative studies that assess the scalability of downscale models to real-world drilling conditions. This study fills this gap by systematically comparing torsional vibrations in downscale and upscale drill strings under different torque conditions at three different depths, shedding light on scaling effects in drilling vibrations. Numerical simulation was carried out taking into account non-linear interactions, damping effects, and torque variations. The laboratory set-up was for a well length of 15 m and was geometrically scaled to represent an upscale well of 450 m. Certain operational parameters such as rotation speed, torque, density, and friction coefficients were modified to keep realistic dynamic behavior, and all models were run at an identical speed of rotation to enforce consistency. The results show that both the upscale and downscale models exhibited stick-slip behavior, but differences in vibration intensity and stabilization trends point out how scaling affects torsional dynamics. Notably, the upscale bit first faced higher torsional oscillation than the set rotation speed after overcoming stick-slip before stabilizing, whereas the downscale bit went through prolonged stick-slip instability before synchronization. This study enhances the understanding of scaling effects in torsional drilling vibrations, offering a foundation for optimizing experimental setups and improving predictive modeling in drilling operations. Full article