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Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux–cluster mass relation (Y SZ – M), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the Illustris TNG hydrodynamical simulation, we find a new proxy for cluster mass which combines Y SZ and concentration of ionized gas ( c gas ) : M ∝ Y conc 3 / 5 ≡ Y SZ 3 / 5 ( 1 − c gas ) Y conc reduces the scatter in the predicted M by ∼ 20 – 30% for large clusters (M ≳ 1014 h −1 M ☉), as compared to using just Y SZ. We show that the dependence on c gas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Y conc on clusters from CAMELS simulations and show that Y conc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4.

This study analyses cloudburst events in the Indian Himalayas from 1970 to 2024, revealing an increasing frequency, particularly in Uttarakhand, Himachal Pradesh, Jammu & Kashmir, and Ladakh. Uttarakhand is the most affected area, Himachal Pradesh experiences frequent events while Jammu & Kashmir and Ladakh show rising trends. Hydrodynamic modelling employed MIKE Hydro River, MIKE 21 FM, and MIKE FLOOD to simulate the events, supported by historical rainfall and discharge data. The MIKE Hydro River NAM model calibrated (2015–2020) and validated (2021–2023), achieved R 2 values of 0.98 and 0.87, and water balance errors of 0.89% and 0.83%, respectively, confirming its reliability for hydrological simulations. A 2D hydrodynamic model simulated the July 16–18, 2018, cloudburst in Uttarkashi, calibrated using Sentinel-1 C-band data with 91.3% inundation extent accuracy. The August 18, 2019, Uttarkashi cloudburst caused a sharp increase in Bhagirathi River discharge from 1,093 at 04:00 to a peak of 5,729 m 3 /s by 14:00, resulting in severe flooding and widespread destruction. This event was simulated using the MIKE 21 FM hydrodynamic model over a 12-h period (02:00–14:00), showing an 8-m water level rise in low-lying areas, causing severe infrastructure damage. The integrating of MIKE FLOOD with MIKE OPERATION and AWS for real-time cloudburst prediction highlights the role of early warning systems in improving preparedness, reducing damage, and saving lives during extreme weather.

In nature, the lateral line of fish is a peculiar and important organ for sensing the surrounding hydrodynamic environment, preying, escaping from predators and schooling. In this paper, by imitating the mechanism of fish lateral canal neuromasts, we developed an artificial lateral line system composed of micro-pressure sensors. Through hydrodynamic simulations, an optimized sensor structure was obtained and the pressure distribution models of the lateral surface were established in uniform flow and turbulent flow. Carrying out the corresponding underwater experiment, the validity of the numerical simulation method is verified by the comparison between the experimental data and the simulation results. In addition, a variety of effective research methods are proposed and validated for the flow velocity estimation and attitude perception in turbulent flow, respectively and the shape recognition of obstacles is realized by the neural network algorithm.

Triggered by the need for developing a comprehensive flood protection strategy (referred to as the Master Plan) for Western Thessaly in Central Greece, we demonstrate a strategic approach for large-scale hydrosystems, where the need for design accuracy is contrasted to extreme computational burden. The area of interest occupies approximately 6,400 km 2 , thus constituting a mega-scale hydrological, hydraulic and water management system, which poses multiple conceptual and computational challenges. The overall question is to provide a synthesis of flood protection solutions and prioritize them under a multipurpose prism. The core methodological framework comprises two axes: (i) a semi-distributed representation of the rainfall-runoff transformations and flood routing processes across the entire study area, and (ii) a coupled 1D/2D hydrodynamic simulation, targeted only over the flood prone riverine system and the highly complex network of main artificial channels. Key results show reductions in flood extents by up to 15% in high-risk areas and significant attenuation of peak flows (averaging to 56% across all dam sites). The final planning prioritizes the strengthening of flood protection through the combined influence of a set of large-scale projects, i.e., dikes, multi-purpose dams and retention basins of controlled inundation. The cornerstone objective is to sketch a framework for facing similar studies in a holistic manner, while maintaining a high level of computational efficiency and explainability.

This study explores the integration of remote sensing data into rain-on-grid flood modelling, with a focus on post-event validation using high-resolution orthophotos. The aim is to assess the hydrologic and hydraulic characteristics of the flood event that occurred on August 11, 2021, in Ezine Creek, which flows through the Bozkurt district center located in the north of Kastamonu, Turkey. By comparing the simulated flood extent with observed flood boundaries extracted from orthophotos, the hydraulic model was calibrated to better represent the actual flood behaviour. The resulting hydrograph was then used in the downstream section, where hydraulic structures and debris that contributed to the flooding were explicitly modelled and compared against observed outcomes. This calibration process enabled the derivation of a realistic hydrograph for downstream boundary conditions in subsequent flood event simulations. The approach demonstrates the value of combining remote sensing and traditional hydraulic modelling for enhanced flood risk assessment and decision-making.

In comparison to single-row cascades, tandem cascades offer the advantages of reduced losses and enhanced operational capabilities, making them widely employed in compressor applications. However, current research on tandem cascades in hydraulic equipment remains relatively limited. In order to explore the potential application of two-dimensional tandem cascade structures in hydrodynamics and investigate their performance differences from single-row cascades, this study proposes a design scheme for a tandem cascade based on an existing single-row cascade design. Numerical simulation technology is utilized to compare and analyze the impact of these two designs on various flow losses under identical working conditions. The results indicate that compared to single-row vanes, the vane configuration of a serial-row design can better reduce losses and increase the pressure difference between the upper and lower surfaces of the vanes, thereby enhancing their load-bearing capacity and stability. This research finding is expected to provide valuable insights for future water pump design and optimization.

With the increased use of automotive engine start-stop systems, the numerical prediction and reduction of frictional losses in sliding bearings during starting and stopping procedures has become an important issue. In engineering practice, numerical simulations of sliding bearings in automotive engines are performed with statistical asperity contact models with empirical values for the necessary surface parameters. The aim of this study is to elucidate the applicability of these approaches for the prediction of friction in sliding bearings subjected to start-stop operation. For this purpose, the friction performance of sliding bearings was investigated in experiments on a test rig and in transient mixed elasto-hydrodynamic simulations in a multi-body simulation environment (mixed-EHL/MBS). In mixed-EHL/MBS, the extended Reynold’s equation with flow factors according to Patir and Cheng has been combined on the one hand with the statistical asperity contact model according to Greenwood and Tripp and on the other hand with the deterministic asperity contact model according to Herbst. The detailed comparison of simulation and experimental results clarifies that the application of statistical asperity contact models with empirical values of the necessary inputs leads to large deviations between experiment and simulation. The actual distribution and position of surface roughness, as used in deterministic contact modelling, is necessary for a reliable prediction of the frictional losses in sliding bearings during start-stop operation.

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R2) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management.

Urban street layout is an important factor in the formation process, characteristics, and risk level of urban flooding; therefore, this study numerically investigates the impact of street layout on urban flood risk to people and vehicles. Four typical street-layout scenarios with areas of 3 km × 3 km are established based on a block-scale investigation. The layout types are regular grid, irregular grid, radial, and annular. Urban inundation models are then constructed for these typical street layouts based on the two-dimensional (2D) hydrodynamic method. Two historic, extreme rainfall events, which occurred in Beijing on July 21, 2012 and in Zhengzhou on July 20, 2021, are used as rainstorm scenarios for urban inundation modelling. The flood risks to people and vehicles are then calculated. Results show that, for an extreme rainstorm on the block scale, the street layout impacts the spatial and temporal distributions of the inundation variables, which include the water depth, flow velocity, flood volume, and inundated area. Moreover, for the same extreme-rainfall scenario, the greatest differences in the total flood volume, maximum street-water depth, and maximum street-flow velocity caused by street-layout differences are 17.22%, 60.25%, and 61.50%, respectively. Among the four street layouts considered in this study, the annular street layout exhibits the lowest degrees of inundation and flood risk. For the same extreme-rainfall scenario, the proportions of high-risk road sections for adults and children in this layout are 58.89% and 62.28% smaller than those for the layout with the largest proportion of high-risk road sections, respectively; the proportions of high-risk road sections for the Honda Accord and Audi Q7 were 55.31% and 53.04% smaller, respectively. The findings of this study may aid scientific understanding and development of “flood-sensitive” block-scale street layouts and urban planning in the context of the changing environment.