Simulation Predictions Research Articles

Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal

Accurate simulation and stream flow prediction are essential for effective water resources planning and management. Hydrological models have long been used for rainfall-runoff modeling; in recent years, with the availability of large datasets, machine learning algorithms, and deep learning techniques have become powerful tools to explore. This study compares rainfall-runoff simulation between the Long and Short-Term Memory (LSTM) (a deep learning model) and the Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS) (a conceptual model) for a steep mountainous catchment in Nepal. During calibration (1989–2006) and validation (2007–2016), the HEC-HMS model demonstrated a root mean square error (RMSE) and Nash Sutcliffe Efficiency (NSE) of 18.3 and 0.73 and 22.93 and 0.66, respectively, for the calibration and validation periods. During the same periods, the LSTM model showed values of 7.25 and 0.87 and 19.59 and 0.64, respectively, for the calibration and validation periods. The performance indices show that the LSTM model has performed better than the HEC-HMS model. The outcomes of this study suggest that the LSTM deep learning model can simulate the discharge successfully across steep mountainous catchments characterized by monsoon and snowmelt runoff, even with a wide range of precipitation variation, making it an asset for hydrological modeling and water resources management in the data-scarce region.

Research on Failure Pressure Prediction of Water Supply Pipe Based on GA-BP Neural Network

The water supply pipeline is regarded as the “lifeline” of the city. In recent years, pipeline accidents caused by aging and other factors are common and have caused large economic losses. Therefore, in order to avoid large economic losses, it is necessary to analyze the failure prediction of pipelines so that the pipelines that are going to fail can be replaced in a timely manner. In this paper, we propose a method for predicting the failure pressure of pipelines, i.e., a genetic algorithm was used to optimize the weights and thresholds of a BP neural network. The first step was to determine the topology of the neural network and the number of input and output variables. The second step was to optimize the weights and thresholds initially set for the back propagation neural network using a genetic algorithm. Finally, the optimized back-propagation neural network was used to simulate and predict pipeline failures. It was proved by examples that compared with the separate back propagation neural network model and the optimized and trained genetic algorithm-back propagation neural network, the model performed better in simulation prediction, and the prediction accuracy could reach up to 91%, whereas the unoptimized back propagation neural network model could only reach 85%. It is feasible to apply this model for fault prediction of pipelines.

Elucidating the Hydrolysis and Polymerization Reactions of Al3+‐Solvated Molecules by Reactive Molecular Dynamics Simulation

ABSTRACTUtilizing the reactive molecular dynamics (ReaxFF MD) simulation, we conducted a comprehensive study on the impact of basicity (OH−/Al3+ ratio), concentration, and temperature on the hydrolysis and polymerization reactions of Al3+‐solvated molecules. Through simulations, we analyzed the structural changes, energy fluctuations of the system, and the evolution patterns of reaction products under different parameters, which were subsequently validated by experimental data. The research results indicate that hydroxide ions in the solution directly influence the breakage of OH bonds in the coordinating water molecules of solvated aluminum ions. This, in turn, affects the number of H2O and OH− ions coordinated with Al3+, leading to changes in hydrolysis products. Additionally, the number of OH− ions surrounding Al3+ affects the electrostatic repulsion, making it easier for polymerization reactions to occur as the system approaches the point of zero charge. On the other hand, an increase in concentration and temperature enhances the frequency of cluster collisions, thus contributing to an increase in polymerization degree. The experimental results align closely with our simulated predictions. As the pH value increases, the particle size exhibits a trend of first increasing and then decreasing, reaching a maximum at the point of zero charge. Simultaneously, an increase in concentration also prompts an increase in particle size. The combination of these empirical results with simulations enhances the credibility and reliability of our model's predictive capabilities. This study not only expands our understanding of the relevant chemical reaction processes but also provides important theoretical support for practical applications in related fields.

Schwarzschild modelling of barred s0 galaxy NGC 4371

ABSTRACT We apply the barred Schwarzschild method developed by Tahmasebzadeh et al. (2022) to a barred S0 galaxy, NGC 4371, observed by IFU instruments from the TIMER and ATLAS3D projects. We construct the gravitational potential by combining a fixed black hole mass, a spherical dark matter halo, and stellar mass distribution deprojected from 3.6 μm S$^4$G image considering an axisymmetric disc and a triaxial bar. We independently modelled kinematic data from TIMER and ATLAS3D. Both models fit the data remarkably well. We find a consistent bar pattern speed from the two sets of models with $\Omega _{\rm p} = 23.6 \pm 2.8 \, \mathrm{km \, s^{-1} \, kpc^{-1} }$ and $\Omega _{\rm p} = 22.4 \pm 3.5 \, \mathrm{km \, s^{-1} \, kpc^{-1} }$, respectively. The dimensionless bar rotation parameter is determined to be $\mathcal {R} \equiv R_{\rm cor}/R_{\rm bar}=1.88 \pm 0.37$, indicating a likely slow bar in NGC 4371. Additionally, our model predicts a high amount of dark matter within the bar region ($M_{\rm DM}/ M_{\rm total}$$\sim 0.51 \pm 0.06$), which, aligned with the predictions of cosmological simulations, indicates that fast bars are generally found in baryon-dominated discs. Based on the best-fitting model, we further decompose the galaxy into multiple 3D orbital structures, including a BP/X bar, a classical bulge, a nuclear disc, and a main disc. The BP/X bar is not perfectly included in the input 3D density model, but BP/X-supporting orbits are picked through the fitting to the kinematic data. This is the first time a real barred galaxy has been modelled utilizing the Schwarzschild method including a 3D bar.

Impact of Atomic Defects on Water Contact Angle of 2D Molybdenum Disulfide Surfaces.

Interfacial dynamics within nanofluidic systems are crucial for applications like water desalination and osmotic energy harvesting. Understanding these dynamics can inform the rational optimization of two-dimensional (2D) materials and devices for such applications. This study explores the wetting behavior of realistic 2D MoS2 surfaces incorporating vacancies and atomic steps, known as atomic defects. We employ a combined density functional theory (DFT) and molecular dynamics (MD) computational approach to elucidate the influence of atomic defects on the MoS2-water interface. DFT calculations are utilized to determine the charge distribution within MoS2. Subsequently, free energy calculations are obtained through MD simulations of the MoS2-water interface. Our findings underscore the importance of incorporating atomic defects into MoS2 surfaces for accurate water contact angle (WCA) predictions in nanofluidic simulations, particularly when using Abal et al. force field parameters. However, the force field developed by Liu et al. yielded more accurate results for pristine MoS2 surfaces. While these parameters provide reliable outcomes for pristine MoS2 surfaces, their application to surfaces with defects may lead to underestimation of WCA. This highlights the critical need for realistic surface representations in nanofluidic modeling to accurately capture the complex interactions between water and MoS2 materials.

Analysis of an Infectious Disease Vaccination Prediction System Based on the MF-Conv LSTM Model

Infectious diseases can seriously threaten people's life safety and have a serious impact on social stability. Therefore, it should improve society’s stability under infectious diseases and ensure the safety of people's lives. A personnel flow feature extraction model based on Multi-Feature Convolutional Long Short-Term Memory (MF-Conv LSTM) is designed based on the characteristics of human daily activity behavior. This can optimize the accuracy of transmission simulation prediction for infectious disease vaccination. When using multi-feature ensemble analysis to extract human daily activity features as input for infectious disease simulation and prediction models, the learner's prediction score for the recurrent infectious diseases reached 0.8705. When using multi-feature ensemble analysis, the predicted scores did not exceed 0.85. The designed infectious disease vaccine transmission prediction model can accurately simulate the infectious behavior of infectious diseases. This provides direction for developing strategies to disrupt the infectious diseases’ spread. This reduces the infectious diseases’ harm to people's personal safety and improves social stability during the spread of large-scale infectious diseases.

Weak lensing combined with the kinetic Sunyaev–Zel’dovich effect: a study of baryonic feedback

ABSTRACT Extracting precise cosmology from weak lensing surveys requires modelling the non-linear matter power spectrum, which is suppressed at small scales due to baryonic feedback processes. However, hydrodynamical galaxy formation simulations make widely varying predictions for the amplitude and extent of this effect. We use measurements of Dark Energy Survey Year 3 weak lensing (WL) and Atacama Cosmology Telescope DR5 kinematic Sunyaev–Zel’dovich (kSZ) to jointly constrain cosmological and astrophysical baryonic feedback parameters using a flexible analytical model, ‘baryonification’. First, using WL only, we compare the $S_8$ constraints using baryonification to a simulation-calibrated halo model, a simulation-based emulator model, and the approach of discarding WL measurements on small angular scales. We find that model flexibility can shift the value of $S_8$ and degrade the uncertainty. The kSZ provides additional constraints on the astrophysical parameters, with the joint WL + kSZ analysis constraining $S_8=0.823^{+0.019}_{-0.020}$. We measure the suppression of the non-linear matter power spectrum using WL + kSZ and constrain a mean feedback scenario that is more extreme than the predictions from most hydrodynamical simulations. We constrain the baryon fractions and the gas mass fractions and find them to be generally lower than inferred from X-ray observations and simulation predictions. We conclude that the WL + kSZ measurements provide a new and complementary benchmark for building a coherent picture of the impact of gas around galaxies across observations.

The Time-Varying Characteristics of Relative Permeability in Oil Reservoirs with Gas Injection

Relative permeability is a critical parameter in reservoir numerical simulation and production prediction, intimately associated with reservoir architecture and fluid property. During gas injection development, substantial alterations in reservoir properties and fluid phase behavior induce dynamic changes in relative permeability. Clearly characterizing the time-varying features of relative permeability is very useful for an understanding of how gas injection influences fluid mobility within the reservoir and enhances recovery rates. In this paper, core displacement experiments are firstly conducted to obtain the characteristics of the relative permeability of oil and gas under various development stages and displacement conditions, further delineating the comprehensive shifts in reservoir properties at different gas injection stages. Subsequently, a novel reservoir numerical simulation method is proposed that considers the spatial and temporal segmentation of relative permeability curves in the reservoir simulation. Finally, a practical application is presented to clarify the effects of injection and production parameters on the development performance of gas flooding oil reservoirs. The results show the following: (i) Significant time-varying characteristics of relative permeability occur throughout gas injection development, in the early stages of gas injection, where most of the reservoir is at the gas injection front, and a rightward shift in relative oil and gas permeability indicates that gas injection promotes oil mobility. Conversely, in the later stages of gas injection, as the reservoir reaches the trailing edge of gas injection, the change trend in relative oil and gas permeability reverses, shifting leftward, thereby exacerbating the gas breakout phenomena. (ii) Increasing the rate of gas injection causes relative oil and gas permeability to move leftward, effectively enhancing the gas volume sweep coefficient and microscopic oil displacement efficiency at lower injection speeds while reducing development performance at higher injection speeds. (iii) An increase in gas injection pressure causes relative oil and gas permeability to shift rightward, and although it reduces residual oil saturation and enhances microscopic oil displacement efficiency, it also intensifies gas breakout phenomena and lowers the gas volume sweep coefficient. This paper provides theoretical guidance and technical support for the design of gas injection strategies, optimization of injection and production parameters, and production forecasting.

Simulasi Metode Monte Carlo untuk Mengatur Sistem Antrian Truk

This research focuses on optimizing pozzolan truck queue management at PT. Danas Putra Mandiri through the application of the Monte Carlo method. The main objective of this research is to develop and implement a simulation application that is able to predict and calculate the average availability of trucks in a certain time unit, with the ultimate goal of increasing the company's operational efficiency. In industries that rely on the transportation of raw materials such as pozzolan, effective truck queue management is key to avoiding distribution delays and reducing operational costs. A queue is a service from one or more services that is caused by the need for services exceeding the capacity of the service or service facilities, so that customers who arrive cannot immediately receive service due to busyness in the service. The method used in this research is the Monte Carlo method. Monte Carlo is an experiment of various elements of probability using random samples. The Monte Carlo method is useful for solving quantitative problems with real or physical processing. This method has the ability to simulate and manage queues that occur in companies. PT Danas Putra Mandiri is one of the companies operating in the mining sector which supplies pozzolan to PT Semen Padang. The pozzolan delivery process uses trucks. The delivery process using trucks can affect the availability of the number of trucks in the company. The data used in this research is data from January 2023 - December 2023 with a total of 1619 data. Data taken through the admin of PT. DANAS PUTRA MANDIRI. Based on simulation predictions of queues on trucks, results were obtained with an average accuracy of 80.6%. The queuing simulation results show that the application of the Monte Carlo method can effectively reduce truck waiting times and increase the availability of trucks for rental, which ultimately contributes to increasing the company's operational efficiency.

Chemical abundances along the quasar main sequence

Context. The main sequence of quasars has emerged as a powerful tool for organizing the observational and physical characteristics of type-1 active galactic nuclei (AGNs). Aims. In this study, we present a comprehensive analysis of the metallicity of the gas in the broad-line region, incorporating both new data and previously published findings, to assess the presence of any trend along the main sequence. Methods. We performed a multicomponent analysis on the strongest ultraviolet (UV) and optical emission lines for a sample of 13 radio quiet quasars in the 0.009 ≤ z ≤ 0.472 redshift range, selected based on the availability of multiwavelength data. We employed UV and optical data obtained from the Hubble Space Telescope (mainly from the Cosmic Origins Spectrograph and Faint Object Spectrograph) and several ground-based observatories, respectively. We then measured ten diagnostic ratios and compared them with the prediction of CLOUDY photoionization simulations, identifying the closest photoionization solution to the data. Results. Our investigation reveals a consistent pattern along the main sequence. We observe a systematic progression in metallicity, ranging from subsolar values to metallicity levels several times higher than solar values. Conclusions. These findings underscore the fundamental role of metallicity in correlating with the main sequence of quasars. Extreme metallicity values, at least several dozen times the solar metallicity, are confirmed in low-z AGNs radiating at a high Eddington ratio, although the origin of the extreme enrichment remains open to debate.

A Precise Prediction of the Chemical and Thermal Shrinkage during Curing of an Epoxy Resin.

A precise prediction of the cure-induced shrinkage of an epoxy resin is performed using a finite element simulation procedure for the material behaviour. A series of experiments investigating the cure shrinkage of the resin system has shown a variation in the measured cure-induced strains. The observed variation results from the thermal history during the pre-cure. A proposed complex thermal expansion model and a conventional chemical shrinkage model are utilised to predict the cure shrinkage observed with finite element simulations. The thermal expansion model is fitted to measured data and considers material effects such as the glass transition temperature and the evolution of the expansion with the degree of cure. The simulations accurately capture the exothermal heat release from the resin and the cure-induced strains across various temperature profiles. The simulations follow the experimentally observed behaviour. The simulation predictions achieve good accuracy with 2-6% discrepancy compared with the experimentally measured shrinkage over a wide range of cure profiles. Demonstrating that the proposed complex thermal expansion model affects the potential to minimise the shrinkage of the studied epoxy resin. A recommendation of material parameters necessary to accurately determine cure shrinkage is listed. These parameters are required to predict cure shrinkage, allow for possible minimisation, and optimise cure profiles for the investigated resin system. Furthermore, in a study where the resin movement is restrained and therefore able to build up residual stresses, these parameters can describe the cure contribution of the residual stresses in a component.

High strain-rate strength response of single crystal tantalum through in-situ hole closure imaging experiments

The properties of crystalline materials often depend on directionality and operating conditions. Specifically, the strength of materials can depend anisotropically on crystal direction and the loading condition. To probe these effects, a preliminary series of high strain-rate (>105/s) strength plate-impact hole closure experiments were performed on high purity single crystal tantalum cubes. The orientation of the single crystals with respect to impact/loading were varied to provide data to inform crystal plasticity modeling efforts. The experiments consist of in-situ high-resolution X-ray radiographic imaging of the hole collapse under dynamic compression conditions to infer the material strength via its resistance to closure at increasing levels of plastic strain. The experiments are compared against hydrocode simulation predictions. A comparison with simple elastic perfectly plastic strength model predictions is presented to elucidate the response of the different crystal orientations at high strain-rate and large plastic strains.

Springback characteristics and influencing laws of four-axis flexible roll bending forming for aluminum alloy.

This study aims to solve the problem of springback control of aluminum alloy components in the rolling process, and the method of combining experiment and simulation is adopted. Firstly, a series of aluminum alloy samples are designed, and the four-axis flexible bending machine is used for precision roll bending. Secondly, the three-dimensional (3D) shape change data of the workpiece before and after roll bending is monitored and recorded in real-time by a high-precision 3D scanner. Meanwhile, aiming at different rolling process parameters of each group (including roll bend speed, feed rate, pre-deformation amount, mold curvature radius, and other factors), advanced finite element software is used to carry out detailed simulation and calculations. In addition, the coincidence is compared and analyzed between the actual experiment results and the simulation prediction. The stress-strain distribution and springback evolution of aluminum alloy during roll bending are described accurately. The experimental and simulation results show that the springback rate of aluminum alloy fluctuates in the range of 5% to 15% after four-axis flexible roll bending, and the specific springback value is influenced by various process parameters. For example, under the premise of keeping other conditions unchanged, when the roll bending speed is increased from 30mm/s to 60mm/s, the springback rate shows an upward trend of about 3%. By increasing the feed rate by 20%, an average decrease of about 7% in springback quantity is observed. It can be seen that the increase in roll bending speed can aggravate the springback phenomenon, and the appropriate increase in feed rate can play a certain role in restraining the springback. Further analysis shows that the choice of the mold curvature radius and pre-deformation amount also has a decisive influence on the springback characteristics. There is a nonlinear relationship between the two parameters and the amount of springback. Changing these two parameters in a specific range can effectively regulate the springback effect.

A quasi-dimensional model of multiple-cycle fuel film evaporation in a port fuel injection spark-ignition methanol marine engine

For a port fuel injection (PFI) spark-ignition (SI) methanol marine engine, the proportion of residual methanol (Xf) depending on the fuel film evaporation (FFE) plays a crucial role in the fuel-air mixing process, combustion performance and emissions. Therefore, a quantitative assessment method on quasi-steady-state FFE was proposed and implemented to calculate the Xf, which was utilized to develop a novel FFE model aimed at improving the predictability of engine simulation. Then, the impact mechanism of injection timing on the engine's performance and emissions under different intake pressure conditions was revealed utilizing the Xf. Delaying the end of injection timing from −400°CA to −180°CA resulted in an early decrease of Xf, and then began to increase after the Xf reversal point. The shortest combustion duration (CA10-90) and the largest brake thermal efficiency (BTE) were achieved at higher Xf levels. Furthermore, optimizing injection timing could reduce HC and NOx emissions by up to 42.38 % and 48.54 %, respectively, though such adjustments need to be carefully tailored to different loads. Finally, a 1-D engine simulation incorporating the FFE model has been constructed and successfully replicated the effects of fuel film evaporation on engine combustion.

Investigating PEDOT:PSS Binder as an Energy Extender in Sulfur Cathodes for Li-S Batteries.

Although lithium-sulfur (Li-S) batteries offer a high theoretical energy density, shuttling of dissolved sulfur and polysulfides is a major factor limiting the specific capacity, energy density, and cyclability of Li-S batteries with a liquid electrolyte. Cathode host materials with a microstructure to restrict the migration of active material may not totally eliminate the shuttling effect or may create additional problems that limit the full dissolution and redox conversion of all active cathode materials. Selecting a cathode coating binder with a multifunctional role offers a universal solution suitable for various cathode hosts. PEDOT:PSS is investigated as such a binder in this study via experimental testing and material characterization as well as multiscale modeling. The study is based on Li-S cells with a sulfur cathode in hollow porous particles as the cathode host and the 10 wt % PEDOT:PSS binder and electrolyte 1 M LiTFSI in 1:1 DOL:DME 1:1 v/v. A reference supercapacitor cell with the same electrolyte and electrodes comprising a coating of the same hollow porous particles and 10 wt % PEDOT:PSS revealed the pseudocapacitive effect of PEDOT:PSS following a surface redox mechanism that dominates the charge phase, which is equivalent to the discharge phase of the Li-S battery cell. A multipore continuum model for supercapacitors and Li-S cells is extended to incorporate the pseudocapacitive effects of PEDOT:PSS with the Li+ ions and the adsorption effects of PEDOT:PSS with respect to sulfur and lithium sulfides in Li-S cells, with the adsorption energies determined via molecular and ab initio simulations in this study. Experimental data and predictions of multiscale simulations concluded a 7-9% extension of the specific capacity of Li-S battery cells due to the surface redox effect of PEDOT:PSS and elimination of lithium sulfides from the anode by slowing down their migration and shuttling via their adsorption by the PEDOT:PSS binder.

The Universe SPHEREx Will See: Empirically Based Galaxy Simulations and Redshift Predictions

We simulate galaxy properties and redshift estimation for SPHEREx, the next NASA Medium Class Explorer. To make robust models of the galaxy population and test the spectrophotometric redshift performance for SPHEREx, we develop a set of synthetic spectral energy distributions based on detailed fits to COSMOS2020 photometry spanning 0.1–8 μm. Given that SPHEREx obtains low-resolution spectra, emission lines will be important for some fraction of galaxies. Here, we expand on previous work, using better photometry and photometric redshifts from COSMOS2020 and tight empirical relations to predict robust emission-line strengths and ratios. A second galaxy catalog derived from the GAMA survey is generated to ensure the bright (m AB < 18 in the i band) sample is representative over larger areas. Using template fitting to estimate photometric continuum redshifts, we forecast the recovery of 19 million galaxies over 30,000 deg2 with redshifts better than σ z < 0.003(1 + z), 445 million with σ z < 0.1(1 + z), and 810 million with σ z < 0.2(1 + z). We also find through idealized tests that emission-line information from spectrally dithered flux measurements can yield redshifts with accuracy beyond that implied by the naive SPHEREx channel resolution, motivating the development of a hybrid continuum–line redshift estimation approach.

Clinical audit of an artificial intelligence (AI) empowered smile simulation system: a prospective clinical trial

Smile aesthetics is an important factor to consider during orthodontic treatment planning. The aim of the present study is to assess the predictability of Invisalign SmileView for digital AI smile simulation in comparison to actual smile treatment outcomes, using various smile assessment parameters. A total of 24 adult subjects (12 females and 12 males; mean age 22 ± 5.2 years) who chose to be treated using Invisalign were prospectively recruited to have their pretreatment smiles captured using the Invisalign SmileView to simulate their new smiles before treatment was started. Patients were then treated using upper and lower Invisalign aligners with average treatment time of 18 ± 6 months. Full post-treatment records were obtained and full smile frame images of simulated smile and actual final smile of each subject were evaluated by an independent examiner using an objective assessment sheet. Ten smile variants were used to assess the characteristics of the full smile images. Significance level was set at P < 0.05. The ICC for the quantitative parameters showed that there was an overall excellent & good internal consistency (alpha value > 0.7 & > 0.9). The Independent t test was performed amongst the quantitative variables. The P value was not significant for all except maxillary inter canine width (P = 0.05), stating that for the five variables namely; philtrum height, commissure height, smile width, buccal corridor and smile index, actual mean values were similar to the simulation mean values. For the qualitative variables, the Kappa value ranged between 0.66 and − 0.75 which showed a substantial level of agreement between the examiners. Additionally, the Chi square test for the qualitative variables, revealed that the P value was found to be significant in all except lip line. This implies that only the lip line values are comparable. More optimal lip lines, straighter smile arcs and more ideal tooth display were achieved in actual post treatment results in comparison to the initially predicted smiles. Five quantitative smile assessment parameters i.e., philtrum height, commissure height, smile width, buccal corridor, and smile index, could be used as reliable predictors of smile simulation. Maxillary inter canine width cannot be considered to be a reliable parameter for smile simulation prediction. A single qualitative parameter, namely the lip line, can be used as a reliable predictor for smile simulation. Three qualitative parameters i.e., most posterior tooth display, smile arc, and amount of lower incisor exposure cannot be considered as reliable parameters for smile prediction.Trial Registration number and date: NCT06123585, (09/11/2023)

Microscopic Aggregation and Film‐Forming Characteristics of Lubricant Additives on Oil–Water Interface: MD Simulation and Experiments on Water Separability

ABSTRACTThe anti‐emulsification property of lubricating oil is an important index to measure the quality of oil. In this paper, the behaviour of surfactants such as lubricating oil additives at the oil–water interface and the influence of the position of ethylene oxide (EO) and propylene oxide (PO) in the block polyether demulsifier on the demulsification effect were investigated by molecular simulation and experimental verification. The properties of seven lubricating oil additives with different functions and two pairs of isomers were investigated by molecular simulation, and their demulsification effects were verified by experiments. Some simulation results such as interface thickness and density distribution can accurately predict the experimental demulsification effect. Moreover, it was found that the position isomerism of surfactants affected the demulsification performance by changing the lipophilic balance and interface properties. The demulsification performance of sequenced copolymers is generally better than that of anti‐sequenced copolymers. The accurate prediction of molecular dynamics simulation makes the selection of lubricating oil demulsifier more extensive and has practical application value.

Runoff Prediction of Tunxi Basin under Projected Climate Changes Based on Lumped Hydrological Models with Various Model Parameter Optimization Strategies

Runoff is greatly influenced by changes in climate conditions. Predicting runoff and analyzing its variations under future climates are crucial for ensuring water security, managing water resources effectively, and promoting sustainable development within the catchment area. As the key step in runoff modeling, the calibration of hydrological model parameters plays an important role in models’ performance. Identifying an efficient and reliable optimization algorithm and objective function continues to be a significant challenge in applying hydrological models. This study selected new algorithms, including the strategic random search (SRS) and sparrow search algorithm (SSA) used in hydrology, gold rush optimizer (GRO) and snow ablation optimizer (SAO) not used in hydrology, and classical algorithms, i.e., shuffling complex evolution (SCE-UA) and particle swarm optimization (PSO), to calibrate the two-parameter monthly water balance model (TWBM), abcd, and HYMOD model under the four objective functions of the Kling–Gupta efficiency (KGE) variant based on knowable moments (KMoments) and considering the high and low flows (HiLo) for monthly runoff simulation and future runoff prediction in Tunxi basin, China. Furthermore, the identified algorithm and objective function scenario with the best performance were applied for runoff prediction under climate change projections. The results show that the abcd model has the best performance, followed by the HYMOD and TWBM models, and the rank of model stability is abcd &gt; TWBM &gt; HYMOD with the change of algorithms, objective functions, and contributing calibration years in the history period. The KMoments based on KGE can play a positive role in the model calibration, while the effect of adding the HiLo is unstable. The SRS algorithm exhibits a faster, more stable, and more efficient search than the others in hydrological model calibration. The runoff obtained from the optimal model showed a decrease in the future monthly runoff compared to the reference period under all SSP scenarios. In addition, the distribution of monthly runoff changed, with the monthly maximum runoff changing from June to May. Decreases in the monthly simulated runoff mainly occurred from February to July (10.9–56.1%). These findings may be helpful for the determination of model parameter calibration strategies, thus improving the accuracy and efficiency of hydrological modeling for runoff prediction.

Dual neural network for contact temperature and cooling state monitoring in water-lubricated bearings

Harsh operating conditions subject the water-lubricated stern bearings of a ship to a non-uniform stress field, leading to frequent boundary lubrication between the bearing and shaft. Poor lubrication results in local temperature rise due to friction heat, leading to heat aging or even carbonization of the bearings. This study proposes a novel universal monitoring approach based on the internal temperature of water-lubricated bearings (WLBs). Radial basis function neural networks (RBFNN) were established to evaluate the surface temperature and cooling statuses of the bearing surface during rubbing, in which simulation results obtained from a temperature model were used as a training set. The accuracy of the simulation results and prediction model was verified through experimental data. The results showed that the maximum error of predicted contact surface temperatures is only 0.84 °C, 0.02 °C, and 1.00 °C in MAE, MAPE, and RMSE, respectively. The accuracy of cooling conditions identification can be achieved at 91.7 % with 600 s of continuous temperature data. The scheme proposed in this study can effectively predict thermal failures of water-lubricated bearings without requiring extensive experimentation, which promotes the development of intelligent water-lubricated bearings.