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The influence of nanoparticle shape, volume fraction, and temperature on the thermal properties of nanofluids plays a pivotal role in engineering applications. However, there remains a considerable lack of systematic research comprehensively considering these factors to study the similarities and differences in the thermal properties of nanofluids composed of metals and their oxides and to conduct in-depth analyses of their internal mechanisms and characteristics. In this study, molecular dynamics simulations were conducted, employing reversing perturbation non-equilibrium molecular dynamics and non-equilibrium molecular dynamics methods. The thermal conductivity and viscosity of Al–Ar and Al2O3–Ar nanofluids were thoroughly investigated under the various influencing factors. Results reveal that under identical conditions, the thermal conductivity of Al–Ar nanofluid surpasses that of Al2O3–Ar nanofluid, exemplified by values such as 0.1832 W/m K (Al–Ar, 1.5%, cylinder, 86 K) versus 0.17745 W/m K (Al2O3–Ar, 1.5%, cylinder, 86 K). Furthermore, the viscosity of Al–Ar nanofluid is lower than that of Al2O3–Ar nanofluid, demonstrated by values such as 0.0004882 Pa S (Al–Ar nanofluid, 86 K, 2.5%, platelets) compared to 0.008975 Pa S (Al2O3–Ar nanofluid, 86 K, 2.5%, platelets). Subsequently, this study analyzed the difference in thermal conductivity between the two nanofluids from the perspective of microscale interface heat conduction by comparing the phonon density of states curves of Al, Ar, and Al2O3 in the two nanofluids for overlap. Subsequently, through radial distribution function analysis, the viscosity difference between Al–Ar and Al2O3–Ar nanofluids is explained based on nanofluid–solid interface and microstructural considerations. This research addresses the comprehensive lack of comparative studies on the thermal properties of nanofluids formed by metals and their oxides. The internal mechanisms underlying the thermal property differences of nanofluids formed by metals and their oxides were revealed from a microscopic perspective, which holds significant implications for the engineering applications of nanofluids.

Urban flooding is becoming a significant urban epidemic in many nations throughout the world as a result of land use and climate change, and regular heavy rains. In this study, we choose to investigate the viability of Sponge City concepts for flood mitigation based on low impact development (LID) strategies in a 26.66 km2 area situated in Guinea's capital city of Conakry. The Storm Water Management Model (SWMM5) was used to simulate the impact of LID measures on the lowering of runoff peaks and node flooding. Simulation results before and after LID facilities are compared in order to understand how LID measures improve flood mitigation. The study showed that LID techniques effectively reduce runoff, which helps mitigate the effects of flooding. The study demonstrated that all LID, whether used singly or in combination, can lower runoff and flooding. However, when it comes to reducing runoff and flooding, LID-combinations perform better than individual LID implementations. For the five return periods (one, five, 20, 50, and 100 years), the LID-combination reduced runoff by 67.83, 65.02, 50.44, 40.18, and 35.88%, and reduced flooding by 85.32, 72.65, 54.05, 46.17, and 42.80%, respectively. Additionally, the reduction rate of overflow junctions is 100, 100, 80, 67, and 70%, respectively. HIGHLIGHTS Our study's findings demonstrate the importance of low impact development (LID) techniques for managing flooding events in small urban watersheds.; When it comes to reducing runoff and flooding, LID-combinations perform better than individual LID implementations.; The number of overflow junctions can be reduced after the LID implementations.; As the intensity of the rainfall increased in all scenarios, the runoff control efficiency declined.;

Understanding the settling behaviour of particles and the interactions between fluid and particles in channelled fluids is crucial for various natural and industrial processes. The research presented in this study focuses on elucidating the sedimentation modes of dual particles within finite-width channels, employing the Arbitrary Lagrangian-Eulerian (ALE) method in conjunction with Hertz contact theory. We aim to understand how the initial relative position affects the dynamic characteristics of particle settlement. The threshold values of α = 35° and gap ratio (the distance between particles relative to their diameter) L/D = 3.5 are deemed critical. The phenomenon of particles 'kissing' does not manifest when the initial angle α ≤ 35°. As the α increases, the DKT (Drafting-Kissing-Tumbling) process recurs at earlier stage. In all cases where dual-particle are arranged in a vertically aligned pattern (DP1 = DP2 or DP1> DP2), the settling behaviour of particles will eventually achieve a steady state characterized by the DKT process when L/D ≤ 4.0. As the gap ratio (L/D) increases, the time taken for the kissing phenomenon is prolonged. Additionally, the two particles exchange positions and the larger particle taking the lead in the settling process. In scenarios where dual particles are arranged side by side, the kissing phenomenon does not occur. Instead, the trajectory of the particles deviates from the centreline and shifts towards the channel wall due to the Magnus effect when L/D ≤ 2.0. When L/D ≥ 3.0, particles settle as though they were a single particle. In cases involving multiple particles, the hindering effect of the wall becomes significant and can not be ignored, leading to the observation of Rayleigh-Taylor instability development. As a result, the overall settlement pattern tends to be 'concave'. [ABSTRACT FROM AUTHOR]

The mechanisms of thermal conductivity enhancement and the factors influencing viscosity are of great interest in the study of nanofluids, while molecular dynamics (MD) simulations considering nanofluids provide more accurate predictions of microscopic properties than conventional experimental studies. MD simulations of non-equilibrium molecular dynamics and reversing perturbation non-equilibrium molecular dynamics methods were used to study thermal conductivity and viscosity, taking into account a variety of influencing factors, as well as nanoparticle material and volume fraction. Through the analysis of the number density distribution, radial distribution function (RDF), and mean square displacement (MSD), the influences of different nanoparticles (Ag, Cu, Au, and Fe) were described and investigated: Ag particles contribute to 47.0% increase in thermal conductivity of 2.5 vol% nanofluids; Au particles improved the viscosity of 2.5 vol% nanofluids by 20.2%; the number density distribution showed positive linear relationship with the atomic mass; the results of MSD and RDF (mean square displacement and radial distribution function) in combination indicated a positive effect of interfacial nanolayer. The results of this research provide important perspectives for comprehending the impacts of multiple nanoparticles on the micro-thermal properties of nanofluids and also highlight the simulation potential of Au–Ar nanofluids.

Under partial load operating conditions, vortex rope generation in the draft tube of a Francis turbine is considered one of the main reasons for hydro unit vibration. In this paper, a Francis turbine HLA551-LJ-43 in the laboratory was taken as a prototype. Numerical simulations of the entire flow passage were carried out. Four different hydro-turbines were chosen to analyze the effect of vortex suppression, which were named the prototype turbine (N-J), the turbine with J-grooves installed on its conical section (W-J), the one with extending runner cone (C), and the one that considered the J-grooves and the extending runner cone at the same time (J+C). Under the part load conditions in which the vortex rope is easily generated (0.4–0.8 times design flow QBEP), the spectrum characteristics of pressure fluctuation, the morphology of vortex rope, and the energy dissipation based on the entropy production theory in the draft tube were studied. The results show that the three optimized structures W-J, C, and J+C could reduce the pressure pulsation in the conical section of the draft tube, weaken the eccentricity of the vortex rope, and decrease the energy losses in the runner and draft tube. It is worth mentioning that the turbine with a J+C optimized structure had the most potent effect on vortex suppression and energy dissipation. Primarily when operating in deep partial load (DPL) conditions, the efficiency of the turbine with a J+C optimized structure was increased by 13.7% compared to the prototype turbine, and the main frequency amplitude of the pressure pulsation in the draft tube was reduced to 32% of the prototype. [ABSTRACT FROM AUTHOR]

This study develops and verifies two advanced wake models based on super-Gaussian distribution: three-dimensional (3D) super-Gaussian (3DSG) and 3D anisotropic super-Gaussian (3DASG) models. They have a smooth Gaussian–top-hat shape (a combination of Gaussian and top-hat shapes) in the near-wake region that gradually transitions to a Gaussian shape in the far-wake region. These models are based on the law of mass conservation and considers wind shear effect; hence, they can accurately describe asymmetric wind distribution in the vertical direction. Because of this Gaussian–top-hat shape, the model is more accurate in simulating the wake in the near-wake region. The anisotropic model also considers different wake expansion rates in various dimensions, rendering the model more realistic. The accuracy and generality of the two models are verified using four wake data sets obtained from wind tunnel tests and wind field measurements. The validation includes the prediction of the wake profile and relative error of the models. The results show that the two models can well predict the wake distribution of various sizes of turbines at any spatial location in the full-wake region. [ABSTRACT FROM AUTHOR]

In wind turbine wake models, Gaussian models depend on multidimensional integration to ascertain the distribution of wake velocity deficits. These integrations, which often involve complex boundary conditions, significantly enhance the complexity of mathematical computations. Due to the difficulty of obtaining analytical solutions, numerical integration methods such as Monte Carlo or other numerical integration techniques are commonly employed. This study presents a three-dimensional wake model (3DJW) for horizontal axis wind turbines, utilizing the Weibull function to simplify wake deficit characterization instead of traditional Gaussian distribution methods. The 3DJW model considers wind shear effects and mass conservation laws to enhance predictions of vertical wake velocities. By integrating incoming wind conditions and turbine parameters, the model efficiently computes downstream wake velocities, improving computational efficiency. To enhance predictions in the ultra-far wake region, an improved three-dimensional Weibull wake model is proposed using the exponential fitting method. Validation through wind tunnel experiments and wind farm data demonstrates the model's accuracy in predicting wake deficits at the hub height, with relative errors in horizontal and vertical profiles mostly within 5% and 3%, respectively. The proposed model enables accurate and rapid calculation of wake velocities at any spatial location downstream, facilitating enhanced energy utilization and reduced costs. [ABSTRACT FROM AUTHOR]

Background: Systemic inflammation, immunity, and nutritional status are closely related to patients' outcomes in several kinds of cancers. This study aimed to establish a new nomogram based on inflammation-immunity-nutrition score (IINS) to predict the prognosis of extranodal natural killer/T-cell lymphoma (ENKTL) patients. Methods: The clinical data of 435 patients with ENTKL were retrospectively reviewed and randomly assigned to training cohort (n=305) and validation cohort (n=131) at a ratio of 7:3. Cox regression analysis was employed to identify independent prognostic factors and develop a nomogram in the training cohort. Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were employed to assess the performance of the nomogram and compare it with traditional prognostic systems (PINK, IPI, KPI). Internal validation was performed using 1000 bootstrap resamples in the validation cohort. Kaplan-Meier survival analyses were conducted to compare the overall survival (OS) of patients in different risk groups. Results: In the training cohort, in addition to several classic parameters, IINS was identified as an independent prognostic factor significantly associated with the OS of patients. The nomogram established based on the independent prognostic indicators showed superior survival prediction efficacy, with C-index of 0.733 in the training cohort and 0.759 in the validation cohort compared to the PINK (0.636 and 0.737), IPI (0.81 and 0.707), and KPI (0.693 and 0.639) systems. Furthermore, compared with PINK, IPI, and IPI systems, the nomogram showed relatively superior calibration curves and more powerful prognostic discrimination ability in predicting the OS of patients. DCA curves revealed some advantages in terms of clinical applicability of the nomogram compared to the PINK, IPI, and IPI systems. Conclusion: Compared with traditional prognostic systems, the nomogram showed promising prospects for risk stratification in ENKTL patient prognosis, providing new insights into the personalized treatment. [ABSTRACT FROM AUTHOR]

This study aimed to optimize the real-time, short-term dispatch of water-light complementary systems in plateau areas. A two-layer nested improved particle swarm optimization-stepwise optimization algorithm trial (IPSO-SOAT) model was devised to address the challenges posed by the intermittent, volatile, and random characteristics of renewable energy, leading to difficulties in renewable energy consumption and severe power cuts. The model, was employed to optimize the load distribution of complementary system power stations. The outer layer of the model employs an improved particle swarm optimization algorithm to introduce uncertainty and enhance prediction accuracy. Additionally, regional optimization and robust optimization were incorporated to improve prediction reliability. The objective function was aimed at minimizing the residual load variance. The inner layer of the model employs a stepwise optimization algorithm, coupled with a two-dimensional coding strategy for the hydropower unit, to optimize the operating status of the hydropower station unit. The objective function in this layer minimizes flow consumption. A water-light complementary system was comprehensively analyzed in the context of the southwestern plateau region, considering the complex terrain characteristics. By comparing three scenarios, the superiority and flexibility of the two-level nested model were visualized. The proposed double-layer nesting model minimizes energy and natural resource consumption while ensuring sustainability, resulting in a reduction of 15,644.265 tons of carbon dioxide emissions per year. This technological innovation makes a significant contribution to sustainable development. [ABSTRACT FROM AUTHOR]