Simulation Predictions Research Articles

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 > TWBM > 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.

Modeling of Lithium-Flow Batteries with Aqueous Electrolyte

Flow batteries with aqueous electrolyte at the cathode are safe, low-cost and ecofriendly energy storage systems. When the aqueous cathode is coupled with a Li-metal anode with organic electrolyte, these batteries have extremely high energy densities (over 4,000 Wh/Kg), which makes them attractive not only for grid applications (where flow batteries are often used) but also in maritime applications such long endurance surface vessel systems and wave gliders [1-4]. In this presentation, we derive the mathematical model for lithium-flow batteries and use this model to predict the performance of the batteries under different discharge conditions, cathode microstructures, water flow velocities, etc. The results are then compared to experimental data already available in the literature. The mathematical model is based on coupling the flow equations (Navier-Stokes) with electrochemical equations for reaction rates and using average microscopic models for the cathode. For instance, Fig. 1 shows the voltage of a flow battery as a function of time for different water velocities in a flow battery simulated by solving the above model. For low water velocities, the water is not able to provide enough oxygen in the cathode and the voltage of the battery collapses after a few seconds. However, as long as the water velocity is above approx. 1.2 cm/s, the battery will have enough oxygen to operate continuously; in this case, the higher the flow of the water, the higher the voltage of the battery in the stationary state will be because of lower overpotential losses. In these simulations, the anode contained Li-metal. The cathode had a length of 1 cm (measured in the direction of water flow) and a thickness of 100 μm. The cell was discharged at a small rate of 0.01 mA/cm2. The incoming water had an oxygen concentration of 5 mg/l (which is equal to the oxygen concentration in seawater) and was pumped through the cathode at different velocities. A detailed derivation of the mathematical model and more simulation results and predictions will be presented at the meeting.

Quantum Coding Transitions in the Presence of Boundary Dissipation

We investigate phase transitions in the encoding of quantum information in a quantum many-body system due to the competing effects of unitary scrambling and boundary dissipation. Specifically, we study the fate of quantum information in a one-dimensional qudit chain, subject to local unitary quantum circuit evolution in the presence of depolarizing noise at the boundary. If the qudit chain initially contains a finite amount of locally accessible quantum information, unitary evolution in the presence of boundary dissipation allows this information to remain partially protected when the dissipation is sufficiently weak, and up to timescales growing linearly in the system size L. In contrast, for strong enough dissipation, this information is completely lost to the dissipative environment. We analytically investigate this “quantum coding transition” by considering dynamics involving Haar-random, local unitary gates, and confirm our predictions in numerical simulations of Clifford quantum circuits. Scrambling the quantum information in the qudit chain with a unitary circuit of depth O(logL) before the onset of dissipation can perfectly protect the information until late times. The nature of the coding transition changes when the dynamics extend for times much longer than L. We further show that at weak dissipation, it is possible to code at a finite rate, i.e., a fraction of the many-body Hilbert space of the qudit chain can be used to encode quantum information. Published by the American Physical Society 2024

In-situ stress field simulation and fracture distribution prediction of middle lower Ordovician in Zone B of Tahe Oilfield, Tarim Basin

This study constructed a heterogeneous rock mechanics model through well seismic joint inversion, set up a dual cycle statement to optimize the application of boundary loads, and obtained the current stress field distribution in Zone B. At the same time, we preferred the rock fracture criterion, carried out the quantitative characterization of different natures of fractures, and used the integrated fracture development rate to portray the integrated fracture development situation. Finally, the horizontal stress difference, stress heterogeneity coefficient, and comprehensive fracture rate were proposed to define the development index of fractured reservoirs. The results show that (1) the maximum horizontal principal stress is 102–144 MPa, and the minimum horizontal principal stress is 85–125 MPa; (2) the development rate of tension fractures is 0.03–0.23, the development rate of shear fractures is 0.15–0.54, and the integrated fracture development rate is 0.28–0.79; (3) the development index of fracture-type reservoirs is >2.3, Class I; 1.8–2.3, Class II; When it is lower than 1.8, Grade III. This article attempts to apply the stress field simulation results to the quantitative prediction of reservoir fractures, adding new ideas for the practical application of stress field results.

Modeling local deformation, damage distribution, and phase transformation in zirconia particle-reinforced TRIP steel composites

This study investigates tensile deformation, damage analysis, and the effect of microtexture on a 10 % vol. zirconia particle-reinforced TRIP steel composite. In situ tensile tests and simulations were conducted, with sequential SEM images captured during tensile loading tests and electron backscatter diffraction (EBSD) providing initial crystal orientation data. SEM images were utilized in digital image correlation (DIC) analysis to calculate the local plastic strain distribution using VEDDAC software. Initial micrographs and EBSD data were transformed into geometry files for simulations, employing crystal plasticity with a dislocation density-based model and a newly proposed phase field approach-based ductile-brittle damage criterion for both austenite and ceramic particles. MATLAB's image processing functions were used to compare and validate experimentally observed damage instances with simulation predictions. The effect of microtexture combined with various physical quantities was explored using MTEX. The results show that the simulations accurately predict strain concentration around ceramic particles, with a higher strain observed in the middle region compared to the DIC observations. The simulated strain in the austenitic matrix is found to be 1.4 times higher than the DIC measurements. Quantitative analysis of damage pixels reveals that the proposed critical plastic strain value aligns closely with experimental results, showing an error of - 0.25 % compared to 1.121 % for a smaller strain case. This comprehensive analysis provides insight into the effect of microstructural attributes on material performance and behavior under tensile deformation, leading to a validated microstructural-informed damage-inclusive crystal plasticity model.

Interfacial Dynamics Study of NCM523-Based Semi-Solid-State Lithium-Ion Batteries by Electrochemical Impedance Spectroscopy.

The use of solid electrolytes (SE) in solid-state batteries holds the promise of achieving higher energy densities and enhancing safety. However, current solid-state batteries face significant interface impedance issues, mainly dealing with the effect of the evolution of the solid-solid interface on ion transport. Semi-solid-state batteries (SSB), containing a small amount of liquid electrolyte, serve as appropriate transitional products in the development process of solid-state batteries. More importantly, the clarity of the relevant interface dynamics can provide theoretical guidance for the subsequent all-solid-state batteries. Therefore, this paper investigates SSB through Electrochemical Impedance Spectroscopy (EIS), primarily employing a combination of theoretical modeling, simulation predictions, and experimental analyses to elucidate the complex electrochemical processes within these batteries. Based on detailed exploration of the complex electrochemical processes within SSB, we have discovered additional electrochemical processes beyond Li+ penetration through the solid-electrolyte interphase (SEI) film and charge transfer. We attribute the additional electrochemical reaction processes to the resistance present at the SE/SEI interface of SSB on account of numerical analysis and interface characterization. Furthermore, this interface resistance exhibits a trend of initial decrease followed by continuous increase, elucidating the attribution and numerical variations of various impedance components within the EIS. The application of EIS techniques to analyze ion transport processes in SSB serves as a suitable transition toward achieving all-solid-state batteries as well as provides guidance for subsequent interface optimization of solid-state batteries and propels their transition from laboratory experimentation to commercialization.

Unraveling the Mystery of the Low CO-to-H2 Conversion Factor in Starburst Galaxies: RADEX Modeling of the Antennae

CO emission has been widely used as a tracer of molecular gas mass. However, it is a long-standing issue to accurately constrain the CO-to-H2 conversion factor (α CO) that converts CO luminosity to molecular gas mass, especially in starburst galaxies. We present the first resolved α CO modeling results with multiple Atacama Large Millimeter/submillimeter Array CO and 13CO transition observations at both giant molecular cloud (GMC) scale at 150 pc and kiloparsec scale for one of the closest starburst mergers, the Antennae. By combining our CO modeling results and measurements of 350 GHz dust continuum, we find that most GMCs in the Antennae have α CO values approximately four times smaller than the commonly adopted Milky Way value (4.3). We find that α CO at GMC scales shows a strong dependence on CO intensity, 13CO/CO ratio, and GMC velocity dispersion, which is consistent with various theoretical and simulation predictions. Specifically, we suggest that the 13CO/CO line ratio and the velocity dispersion can be used to infer α CO in starburst regions. By applying our modeled α CO in GMC analyses, we find that GMCs in the Antennae are less gravitationally bound than in normal spiral galaxies, which is more consistent with what is predicted by merger simulations. At kiloparsec scale, we find that our modeled α CO values are smaller than the modeled α CO at GMC scale by 40%, which can be due to inclusion of a diffuse gas component with lower α CO values. We find a similar correlation of α CO and CO intensity at kiloparsec scales to that at GMC scales.

Turbulent circumnuclear disc and cold gas outflow in the newborn radio source 4C 31.04

We present deep kiloparsec- and parsec-scale neutral atomic hydrogen (H I) absorption observations of a very young radio source (≤5000 years), 4C 31.04, using the Westerbork Synthesis Radio Telescope (WSRT) and the Global Very Long Baseline Interferometry (VLBI) array. Using z = 0.0598, derived from molecular gas observations, we detect, at both kpc and pc scales, a broad absorption feature (FWZI = 360 km s−1) centred at the systemic velocity, and narrow absorption (FWZI = 6.6 km s−1) redshifted by 220 km s−1, both previously observed. Additionally, we detect a new blueshifted, broad, shallow absorption wing. At pc scales, the broad absorption at the systemic velocity is detected across the entire radio source while the shallow wing is only seen against part of the eastern lobe. The gas has higher H I column density along the eastern lobe than along the western one. The velocity dispersion of the gas is high (≥40 km s−1) along the entire radio continuum, and is highest (≥60 km s−1) in the region including the outflow and the radio hot spot. While we detect a velocity gradient along the western lobe and parts of the eastern lobe at PA ∼ 5° −10°, most of the gas along the rest of the eastern lobe exhibits no signs of rotation. Earlier optical spectroscopy suggests that the optical AGN is very weak. We therefore conclude that the radio lobes of 4C 31.04 are expanding into a circumnuclear disc, partially disrupting it and making the gas highly turbulent. The distribution of gas is predominantly smooth at the spatial resolution of ∼4 pc studied here. However, clumps of gas are also present, particularly along the eastern lobe. This lobe appears to be strongly interacting with the clouds and driving an outflow ∼35 pc from the radio core, with a mass-outflow rate of 0.3 ≤ Ṁ ≤ 1.4 M⊙ year−1. It is likely that this interaction has caused the eastern lobe to be rebrightened, giving the source an asymmetric morphology. We compare our observations with the predictions of a recent analytical model regarding the survival of atomic gas clouds in radio-jet-driven outflows and find that the existence of a subkpc-scale outflow in this case could imply inefficient mixing of the cold gas with the hot medium and high gas density, leading to very short cooling times. Overall, our study provides further evidence of the strong impact of radio jets on the cold interstellar medium (ISM) in the early stages of their evolution and supports the predictions of numerical simulations regarding jet–ISM interactions and the nature of the circumnuclear gas into which the jets expand.

An effective simulation scheme for the prediction of aerodynamic environment under hypersonic conditions characterized by NACA0012

Currently, aerodynamic environment prediction research into scramjet-propelled vehicles characterized by NACA0012 under hypersonic conditions is relatively sparse. Two-dimensional external flow field models are established, and then through validation tests, we perform a systematic investigation between simulation parameters and prediction accuracy, and an effective aerodynamic environment prediction simulation scheme under hypersonic conditions is proposed. Unlike under incompressible conditions, the maximum accuracy decline could be attributed to the inappropriate choice of the sharp trailing edge modeling method, but the definition formula is still preferred. In particular, for the two modeling data point sources, Airfoil tools and NACA4, the numerical performance of the latter is better than the former, and the calculation accuracy negatively correlates with the number of data points offered by both of them. Moreover, for the mesh cells near the shock, the cell Reynolds number and aspect ratio values should be no smaller than 16 and not exceed 380, respectively, and the recommended values for the far field distance, the turbulence model and flux type are 16L, Spalart-Allmaras, and ROE flux type. Under hypersonic conditions, the aerodynamic environment characterized by NACA0012 predicts a maximum temperature of approximately 1856.85 °C, with an average temperature change rate of 77 °C/s. Meanwhile, the top sound pressure level and the vibration acceleration could reach up to 145 dB and 182 g, respectively.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

Frequency reconfigurable slot antenna utilizing solid-state plasma S-PIN diodes: simulation and experimental analysis

This article examines the feasibility of integrating solid-state plasma surface p-i-n (S-PIN) diodes into on-chip slots using silicon-on-insulator technology. Two categories of solid-state plasma S-PIN diodes are characterized. Simulations analyze the current–voltage (I–V) characteristics, carrier concentrations, potential distribution, and thermal considerations of both single and dual S-PIN diodes, with optimization of their structural parameters and sizes. Experimental tests on fabricated S-PIN diodes of varying sizes with bias lines embedded in the on-chip slots show favorable I–V characteristics for both types of diodes. The measured results closely align with simulation predictions for the diodes’ conduction onset voltages. Additionally, a cavity-backed slot antenna is proposed to evaluate and compare radiation characteristics resulting from integrating different types of S-PIN diodes.

In-Silico Analyses of Molecular Force Sensors for Mechanical Characterization of Biological Systems.

Mechanical forces play key roles in biological processes such as cell migration and sensory perception. In recent years molecular force sensors have been developed as tools for in situ force measurements. Here we use all-atom steered molecular dynamics simulations to predict and study the relationship between design parameters and mechanical properties for three types of molecular force sensors commonly used in cellular biological research: two peptide- and one DNA-based. The peptide-based sensors consist of a pair of fluorescent proteins, which can undergo Förster resonance energy transfer (FRET), linked by spider silk (GPGGA)n or synthetic (GGSGGS)n disordered regions. The DNA-based sensor consists of two fluorophore-labeled strands of DNA that can be unzipped or sheared upon force application with a FRET signal as readout of dissociation. We simulated nine sensors, three of each kind. After equilibration, flexible peptide linkers of three different lengths were stretched by applying forces to their N- and C-terminal Cα atoms in opposite directions. Similarly, we equilibrated a DNA-based sensor and pulled on the phosphate atom of the terminal guanine of one strand and a selected phosphate atom on the other strand in the opposite direction. These simulations were performed at constant velocity (0.01 nm/ns - 10 nm/ns) and constant force (10 pN - 500 pN) for all versions of the sensors. Our results show how the force response of these sensors depends on their length, sequence, configuration and loading rate. Mechanistic insights gained from simulations analyses indicate that interpretation of experimental results should consider the influence of transient formation of secondary structure in peptide-based sensors and of overstretching in DNA-based sensors. These predictions can guide optimal fluorophore choice and facilitate the rational design of new sensors for use in protein, DNA, hybrid systems, and molecular devices.

A class of chemical compounds enhances clustering of muscle nicotinic acetylcholine receptor in cultured myogenic cells

Neuromuscular signal transmission is affected in various diseases including myasthenia gravis, congenital myasthenic syndromes, and sarcopenia. We used an ATF2-luciferase system to monitor the phosphorylation of MuSK in HEK293 cells introduced with MUSK and LRP4 cDNAs to find novel chemical compounds that enhanced agrin-mediated acetylcholine receptor (AChR) clustering. Four compounds with similar chemical structures carrying benzene rings and heterocyclic rings increased the luciferase activities 8- to 30-folds, and two of them showed continuously graded dose dependence. The effects were higher than that of disulfiram, a clinically available aldehyde dehydrogenase inhibitor, which we identified to be the most competent preapproved drug to enhance ATF2-luciferase activity in the same assay system. In C2C12 myotubes, all the compounds increased the area, intensity, length, and number of AChR clusters. Three of the four compounds increased the phosphorylation of MuSK, but not of Dok7, JNK. ERK, or p38. Monitoring cell toxicity using the neurite elongation of NSC34 neuronal cells as a surrogate marker showed that all the compounds had no effects on the neurite elongation up to 1 μM. Extensive docking simulation and binding structure prediction of the four compounds with all available human proteins using AutoDock Vina and DiffDock showed that the four compounds were unlikely to directly bind to MuSK or Dok7, and the exact target remained unknown. The identified compounds are expected to serve as a seed to develop a novel therapeutic agent to treat defective NMJ signal transmission.

Advances from R2CA project on reactor simulations for burst rod number evaluation during LOCA

In the frame of the “Reduction of Radiological Consequences of design basis and extension accidents” European Union’s Horizon 2020 project, a specific effort was focused on the update and the development of methodologies to assess the radiological consequences of loss-of-coolant accidents. Evaluation of the radiological consequences associated to this accident is strongly linked to the prediction of the rod burst ratio and new approaches were investigated through research and development associated to accident simulation software. Complementarily to approaches chaining system thermalhydraulic simulation to fuel performance code, approaches with integral codes ASTEC, DRACCAR and ATHLET-CD were developed respectively by ENEA, IRSN and HZDR. These applications were demonstrated on LOCA simulation for PWR and highlighted the capabilities of the tools. The ASTEC PWR model was extended by ENEA, which proposes a 2D core model with an increased number of representative fuel rods. HZDR and IRSN proposes approaches based on 3D core model which are able to capture distinctive fuel assembly responses and predict the rod burst ratio according to the distribution of rod behaviors within the core. This work highlighted several possible core models to predict the rod burst ratio and which can be used in integral tools abled to couple thermal hydraulics and thermomechanics. Advanced core models simulating the thermomechanical response of several representative rods per fuel assemblies and using 3D thermal hydraulics model are recommended due to the heterogeneities on power distribution, which impacts flow distribution. The need to model RPV in 3D was underlined by ATHLET-CD large break loss-of-coolant demonstrative case, which exhibits non-symmetric core heat-up.In addition, demonstrative cases proposed by ENEA and IRSN compared predictions obtained with the specific burst criteria developed in the frame of R2CA project and more classical one devoted to the core coolability assessment. The strong sensitivity of the rod burst ratio prediction to the burst criteria was highlighted. The need to handle uncertainties within a global methodology for rod burst ratio evaluation was underlined.Finally, throughout the development of loss-of-coolant accident applications with ASTEC, DRACCAR and ATHLET-CD, some prospects of development were identified for the codes and should bring advances for LOCA simulation and RBR predictions.

Predicting the geospatial distribution of Chinese rice nutrient element in regional scale for the geographical origin—A case study on the traceability of Japonica rice

AbstractEffective geographical origin discrimination of Chinese rice requires a large database of samples to ensure sufficient data for origin verification at a regional scale. In this study, environmental similarity was used to establish a spatial database of rice nutrient element, and then the validity of the database was verified using the back propagation artificial neural networks modeling (BPNN). The spatial distribution model of 14 rice nutrient element (Al, Ba, Ca, Cu, Cr, Fe, K, Mg, Mn, Mo, Na, Ni, Rb, and Zn) on regional scale was built using an environmental similarity method for the first time. Elemental concentrations of 692 samples were used to build a simulated geographical origin prediction model for northeastern (N‐E), middle to lower Yangtze River plain (Y‐R), southwestern (S‐W), and southeastern (S‐E) in China. The results indicated that the performance of the environmental similarity model for these four growing regions was S‐W > N‐E > S‐E > Y‐R based on the lowest ranking root mean square error (RMSE) for each region. For example, the RMSEs of Zn in S‐W, N‐E, S‐E, and Y‐R regions were 2.0, 2.4, 2.7, and 3.7 mg/kg, respectively. A case study on the traceability of Japonica rice was shown that Japonica rice could be discriminated with higher origin accuracy using a simulated database (91.8%) than by the actual database (87.0%) using the BPNN model. This indicates that a simulated rice element database could improve the accuracy of geographical origin discrimination for Chinese rice and potentially be applied to other large national‐scale crop datasets.

End-to-end simulation of particle physics events with flow matching and generator oversampling

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.

One-pot complexation of phytic acid and polyethyleneimine on cellulosic microfibers towards insulative and flame-resistant foam

Flame resistance is required for the deployment of bio-based materials, especially those forming cellular structures that endow thermal insulation. This study proposes a one-pot strategy to prepare cellular lignocellulosic composites with excellent flame resistance. Lignocellulosic microfibers were used as the substrate onto which a flame-retardant complex consisting of P-containing phytic acid (PA) and N-containing polyethyleneimine (PEI) was formed. Following the prediction of ab initio molecular dynamics simulation, PA and PEI are integrated onto MF-CTMP following a single-step complexation assembly triggered by pH effects. The PA-PEI modified MF-CTMP can be readily transformed into a composite solid foam by dewatering a wet foam followed by oven drying. At the expense of a slightly reduced thermal insulation (thermal conductivity increase from 33.6 ± 0.6 to 40.0 ± 0.6 mW/(m·K)) the presence of PA-PEI complexes significantly improved the mechanical performance of the foam and uniquely endows it with flame resistance. Compared to unmodified MF-CTMP foams, the composite foams showed significant improvement in the Young's, specific compression, and flexural moduli (increased by 13.5, 5.5, and 7.3 folds, respectively), a high oxygen index (up to 40.8 %) and self-extinguishing effects. The results suggest the suitability of the introduced lignocellulosic foam as an alternative to traditional synthetic polymer-based counterparts as well as inorganic matter for insulation, particularly relevant to the building sector.

Numerical Simulation and ANN Prediction of Crack Problems within Corrosion Defects.

Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem.