Variation Of Model Parameters Research Articles

Rapid Inverse Parameter Inference Using Physics-Informed Neural Networks

As Li-ion batteries become more essential in today's economy, tools need to be developed to accurately and rapidly diagnose a battery's internal state-of-health. Using a Li-ion battery's (high-rate) voltage response, it is proposed to determine a battery's internal state through Bayesian calibration.However, Bayesian calibration is notoriously slow and requires thousands of model runs. To accelerate parameter inference using Bayesian calibration, a surrogate model is developed to replace the underlying physics-based Li-ion model. Developing a surrogate model for rapid Bayesian calibration analysis is discussed for both the single particle model (SPM) and the pseudo two-dimensional (P2D) model.Surrogate models are constructed using physics-informed neural networks (PINNs) that encode the influence of internal properties on observed voltage responses. In practice, a neural network can be trained by: 1) using simulation results of the physics-based model (i.e., a data-loss approach); 2) using the residuals of the governing equations themselves (i.e., a physics-loss approach); or 3) using a combination of simulation results and governing equation residuals. In the present work, PINNs are developed using a variety of training losses and neural network architectures. In this analysis, it is shown that a PINN surrogate model can be reliably trained with only physics-informed loss. However, using a coupled data-informed and physics-loss approach produced the most accurate PINNs. Figure~\\ref{fig:spm_2d} illustrates the absolute relative errors of trained PINN networks using several different training losses and neural network architectures.After determining a consistent training strategy for both the SPM and P2D PINN surrogate models, the PINNs are extended to determine additional internal state-of-health parameters. As more and more parameters were introduced, the PINN training suffered from ``the curse of dimensionality", which was mitigated by using a hierarchical training approach (where a PINN trained with fewer variable model parameters was used to train a PINN with more variable model parameters).Next, the high-dimensionality PINN surrogates are then integrated into Bayesian calibration schemes to identify internal Li-ion battery properties from experimentally measured voltages. Interpreting the high-dimensional parameter posteriors is discussed with respect to model error, parameter prior choices, and experimental errors. Figure 1

Reconstructing the Pseudo-2-Dimensional (p2D) Model in Comsol Multiphysics Using Mathematical Interfaces

The classic ‘pseudo-2-dimensional’ (p2D) model of Newman and co-workers (also known as the DFN model), with its different efficient implementations, is the state-of-the-art physics-based electrochemical model for lithium-ion batteries. COMSOL Multiphysics (COMSOL) is a popular modeling software for multiphysics simulations, and its Battery Design Module for Li-ion batteries is widely used in the battery modeling community. This work presents a step-wise model development for reconstructing the detailed p2D model equations. This includes variable model parameters and weak-form implementation for the solid-phase diffusion, eliminating the need for non-local couplings and approximations for modeling the pseudo radial-dimension in the solid-phase.To model and understand the effect of capacity degradation on lithium-ion cells, the battery models in COMSOL need to incorporate additional degradation mechanisms. In this work, we have followed the approach of equation-based modeling in COMSOL, which enables us to formulate the p2D model using the mathematical PDE interfaces. This approach provides us with a lot of flexibility in terms of battery degradation model formulation and helps develop a clearer understanding of the p2D model equation in light of the assumptions involved.The steps in the model development involved writing the conservation equations in the generic mathematics interfaces coupled with the additional equations for the degradation mechanisms. The efforts were centered on developing cycling simulations based on Events for practically relevant operating conditions. This custom model was validated against the results from COMSOL’s Battery Interface, in-house codes, and baseline cycle data from the experiments and further developed as a physicochemical degradation model for automotive, large-format Lithium-ion batteries.This equation-based COMSOL model with cycling works without the requirement for Battery Design Module, LiveLink and Application Builder and can be adopted for a variety of electrochemical modeling applications by the electrochemical modeling community.

Interacting quark star with pressure anisotropy and recent astrophysical observations

This paper investigates the internal structure and equation of state (EoS) of quark stars (QS) with pressure anisotropy, incorporating recent astrophysical observations. Utilizing perturbative QCD corrections and color superconductivity, the EoS is expressed as a dimensionless function dependent on a single parameter, providing a comprehensive exploration of strong interaction effects. By rescaling the EoS and introducing dimensionless variables, the study encompasses scenarios ranging from noninteracting quark matter to extreme stiffness characterized by a parameter, λ̄. Anisotropic QS configurations are constructed using a spherically symmetric metric, with locally anisotropic matter described by a generalized EoS for tangential pressure. The Tolman–Oppenheimer–Volkoff equations governing star structure are presented in dimensionless form, and numerical solutions explore the parameter space defined by effective bag constants and λ̄. Mass–radius relations and the compactness of anisotropic QSs are analyzed under various model parameter variations, considering constraints from pulsar masses and gravitational wave phenomena. The study highlights the influence of pressure anisotropy on the maximum mass of QSs and discusses the impact of model parameters on internal properties. Additionally, the paper considers static stability, adiabatic index, and sound velocity profiles, providing a comprehensive understanding of QS behavior. Overall, this investigation contributes valuable insights into the properties of QSs and their compatibility with observational constraints, offering a detailed exploration of their EoS and internal structure.

Modeling the dependence of electrolyte design on lithium-sulfur battery performance

Herein, we offer two electrochemical models, a zero-dimensional (0-D) and a one-dimensional (1-D) model, to mechanistically explore the impact of the electrolyte-to-sulfur (E/S) ratio and electrolyte properties on the discharge behavior of a lithium-sulfur (Li-S) cell. The electrochemically active area in both models is defined based on the carbon weight fraction and reference porosity, so the influence of the carbon loading and E/S ratio on the reaction kinetics is included. Mass transfer of the species must be considered to model the reliance of the discharge capacity on the current density. Instead, the 0-D model is more responsive to the influence of the E/S ratio on the discharge capacity. The effect of electrolyte properties can be modeled implicitly through model parameters. The 0-D model is less sensitive to the variance in model parameters than the 1-D model in terms of the predicted capacity.

A Robust Approach Using M-Estimation for Dynamic Panel Autoregressive Model

This paper presents a robust M-estimation approach for first-order panel autoregressive models, addressing the challenges posed by high persistence levels of the autoregressive parameter and individual heterogeneity. Generalized method of moments estimators widely used in dynamic panel models exhibit substantial finite sample biases and are sensitive to weak instruments, particularly as the autoregressive parameter gets close to unity. Our proposed weighted M-estimator, which uses a power function for the scale parameter in Huber’s loss function, offers a robust alternative. By minimizing the variance of model parameters through an optimal tuning parameter, our method enhances the efficiency and robustness of parameter estimates. We demonstrate the superiority of the proposed approach through several Monte-Carlo simulations and an application to hydro-electric power output data, providing comprehensive comparisons with existing generalized method of moments estimators.

Quantifying Biogeochemical Controls of Open Ocean CDOM From a Global Mechanistic Model

AbstractChromophoric dissolved organic matter (CDOM) is an important part of ocean carbon biogeochemistry with relevance to long‐term observations of ocean biology due to its dominant light absorption properties. Thus, understanding the underlying processes controlling CDOM distribution is important for predicting changes in light availability, primary production, and the cycling of biogeochemically important matter. We present a biogeochemical CDOM model for the open ocean with two classes of biological lability and uncertainty estimates derived from 43 ensemble members that provide a range of model parameter variations. Ensemble members were optimized to match global ocean in situ CDOM measurements and independently assessed against satellite CDOM estimates, which showed good agreement in spatial patterns. Based on the ensemble median, we estimate that about 7% of open‐ocean CDOM is of terrestrial origin, but the ensemble range is large (<0.1–26%). CDOM is rapidly removed in the surface ocean (<200 m) due to biological degradation for short‐lived CDOM and photodegradation for long‐lived CDOM, leading to a net flux of CDOM to the surface ocean from the dark ocean. This deep‐water source (ensemble median 0.001 m−1 yr−1) is similar in magnitude to the riverine flux (0.005 m−1 yr−1) into the surface ocean. Though discrepancies between the model and observational data remain, this work serves as a foundational framework for a mechanistic assessment of global CDOM distribution that is independent of satellite data.

Parameters estimation and sensitivity analysis of lithium-ion battery model uncertainty based on osprey optimization algorithm

To advance the field of lithium-ion battery (LIB) research, this paper unveils an accurate modelling of LIB that primarily relies on the equivalent circuit model, backed by the Osprey Optimization Algorithm (OOA). In the modelling stage, both single and double resistance-capacitance models are evaluated to depict the charge dynamics, incorporating the effects of fading, load, and temperature variations. The OOA approach is utilized to minimize integral squared errors between the actual measured and model-predicted battery voltages under constraints imposed by the model design variables. This approach is applied to a commercial 2.6 Ahr Panasonic LIB, with the performance of the OOA-based model being benchmarked against models developed by means of other optimization algorithms for further validation. Moreover, the robustness of the OOA method is assessed under battery uncertainty conditions or model parameter variation. A sensitivity analysis is performed on the battery model by employing a proposed approach that evaluates the impact of varying each parameter of the battery model by ±5 %, in a sequence that ascends and descends from 0 to 5 %. The single resistance-capacitance model is selected for in-depth validations. Notably, the OOA approach excels in estimating parameters for LIB modeling under both normal and abnormal operating conditions.

Parameter estimation and uncertainty quantification of a fiber-reinforced concrete model by means of a multi-level Bayesian approach

The paper presents a procedure for the stochastic calibration of a cracked hinge model on the basis of an extensive experimental campaign performed on a large group of nominally identical fiber-reinforced specimens. The calibration is carried out in a multi-level Bayesian framework that allows to quantify and separate several uncertainty contributions affecting model parameters. Indeed, the variability in the experimental response for nominally identical specimens due to the material heterogeneity represents a significant uncertainty contribution as well as model error. The former can be quantified at the hyper-parameter level of the multi-level framework. The presented results highlight the good agreement of the numerical predictions with the experimental data and the superior performance of the multi-level framework compared to that of the classical single-level framework. We also perform analyses to explore the impact of the prior parameter model conditioned on hyper-parameters and assess the minimum number of specimen datasets needed to quantify the inherent variability of model parameters.

Bridging the Covid-19 data and the epidemiological model using the time-varying parameter SIRD model

This paper extends the canonical model of epidemiology, the SIRD model, to allow for time-varying parameters for real-time measurement and prediction of the trajectory of the Covid-19 pandemic. Time variation in model parameters is captured using the score-driven modeling structure designed for the typical daily count data related to the pandemic. The resulting specification permits a flexible yet parsimonious model with a low computational cost. The model is extended to allow for unreported cases using a mixed-frequency setting. Results suggest that these cases’ effects on the parameter estimates might be sizeable. Full sample results show that the flexible framework accurately captures the successive waves of the pandemic. A real-time exercise indicates that the proposed structure delivers timely and precise information on the pandemic’s current stance. This superior performance, in turn, transforms into accurate predictions of the death cases and cases treated in Intensive Care Units (ICUs).

Spatial-temporal variability in nitrogen use efficiency: Insights from a long-term experiment and crop simulation modeling to support site specific nitrogen management

Within-field soil heterogeneity can lead to large variation in nitrogen use efficiency (NUE). Crop simulation models provide a multi-faceted approach to management considering both soil and plant interactions. However, research using crop models for investigating within field variation in NUE is limited, in part because of challenges quantifying spatially variable soil model parameters. Here soil apparent electrical conductivity (ECa) and measured soil properties were used to map spatial variations in soil characteristics across a Long-Term Experiment in Norfolk, England. The relationship between plot ECa across the 3 ha experiment and agronomic data across three different nitrogen rates (0, 110, and 220 kg N ha-1) over five wheat years (2010–2020) was quantified. The Sirius crop model was parameterized for two soils representing the extremes of ECa. Sirius was validated using recorded plot data. Site-specific optimal nitrogen and associated leaching risks were simulated across 29 years of weather data. Variation in soil properties had significant impact on measured NUE. At 220 kg N ha-1 mean observed yields across 5 years ranged from 9.0 to 10.7 t ha-1 and grain protein from 11.6% to 11% on the low EC and high EC plots, respectively. On average fertiliser grain N recovery was 19.7 kg N ha-1 lower on the low ECa plots. Sirius simulated the variation in yield, grain protein and grain N recovery to a good level of accuracy with RRMSE of 19.5%, 15.4% and 19.5%, respectively. Simulated optimal nitrogen on the low EC soils was on average 12 kg N ha-1 lower, with >1 in 4 years with optimal nitrogen <200 kg N ha-1. Our work demonstrated that using a combination of proximal soil EC scans and targeted soil sampling we can optimize the data requirements for model parameterisation to support site-specific N management.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Dynamical phase transition in models that couple chromatin folding with histone modifications.

Genomic regions can acquire heritable epigenetic states through unique histone modifications, which lead to stable gene expression patterns without altering the underlying DNA sequence. However, the relationship between chromatin conformational dynamics and epigenetic stability is poorly understood. In this paper, we propose kinetic models to investigate the dynamic fluctuations of histone modifications and the spatial interactions between nucleosomes. Our model explicitly incorporates the influence of chemical modifications on the structural stability of chromatin and the contribution of chromatin contacts to the cooperative nature of chemical reactions. Through stochastic simulations and analytical theory, we have discovered distinct steady-state outcomes in different kinetic regimes, resembling a dynamical phase transition. Importantly, we have validated that the emergence of this transition, which occurs on biologically relevant timescales, is robust against variations in model design and parameters. Our findings suggest that the viscoelastic properties of chromatin and the timescale at which it transitions from a gel-like to a liquidlike state significantly impact dynamic processes that occur along the one-dimensional DNA sequence.

Dynamic analysis and optimal control of stochastic information cross-dissemination and variation model with random parametric perturbations.

Information dissemination has a significant impact on social development. This paper considers that there are many stochastic factors in the social system, which will result in the phenomena of information cross-dissemination and variation. The dual-system stochastic susceptible-infectious-mutant-recovered model of information cross-dissemination and variation is derived from this problem. Afterward, the existence of the global positive solution is demonstrated, sufficient conditions for the disappearance of information and its stationary distribution are calculated, and the optimal control strategy for the stochastic model is proposed. The numerical simulation supports the results of the theoretical analysis and is compared to the parameter variation of the deterministic model. The results demonstrate that cross-dissemination of information can result in information variation and diffusion. Meanwhile, white noise has a positive effect on information dissemination, which can be improved by adjusting the perturbation parameters.

Chemical Potential Analysis as an Alternative to the van't Hoff Method: Hypothetical Limits of Solar Thermochemical Hydrogen.

The van't Hoff method is a standard approach for determining reaction enthalpies and entropies, e.g., in the thermochemical reduction of oxides, which is an important process for solar thermochemical fuels and numerous other applications. However, by analyzing the oxygen partial pressure pO2, e.g., as measured by thermogravimetric analysis (TGA), this method convolutes the properties of the probe gas with the solid-state properties of the examined oxides, which define their suitability for specific applications. The "chemical potential method" is here proposed as an alternative. Using the oxygen chemical potential ΔμO instead of pO2 for the analysis, this method does not only decouple gas-phase and solid-state contributions but also affords a simple and transparent approach to extracting the temperature dependence of the reduction enthalpy and entropy, which carries important information about the defect mechanism. For demonstration of the approach, this work considers three model systems; (1) a generic oxide with noninteracting, charge-neutral oxygen vacancy defects, (2) Sr0.86Ce0.14MnO3(1-δ) alloys with interacting vacancies, and (3) a model for charged vacancy formation in CeO2, which reproduces the extensive experimental TGA data available in the literature. The reduction behavior of these model systems obtained from the chemical potential method is correlated with simulated results for the thermochemical water splitting cycle, highlighting the exceptional behavior of CeO2, which originates from defect ionization. The theoretical performance limits for solar thermochemical hydrogen within the charged defect mechanism are assessed by considering hypothetical materials described by a variation of the CeO2 model parameters within a plausible range.

Joint identification of groundwater pollution source information, model parameters, and boundary conditions based on a novel ES-MDA with a wheel battle strategy

Groundwater pollution source identification (GPSI) is an important prerequisite for pollution remediation and risk assessment. However, an accurate GPSI is usually difficult to achieve due to the need to simultaneously consider practical problems and theoretical research. For practical problems, the boundary conditions, especially the concentration boundary, are often given as known constants through prior information. However, in most practical situations, the boundary conditions are complex and cannot be accurately estimated in advance, which leads to the distortion of the final identification results. Therefore, this study focused on the concentration boundary, and first proposed to jointly identify three types of unknown variables (source information, model parameters, and boundary conditions) to ensure that the identification results had more practical value. For theoretical research, as the number of unknown variable types increases, the difficulty of solving the inverse problem often increases, which may lead to inaccurate inversion results. Thus, a novel ensemble smoother with multiple data assimilation (ES-MDA) with a wheel battle strategy was proposed, enhancing the identification accuracy. We designed two cases to verify the effectiveness and practicality of the above ideas: a low-dimensional Case 1 (including four different synthetic scenarios: three scenarios with unknown boundary conditions under three concentration boundary modes and a scenario with known boundary conditions) and a high-dimensional complex Case 2. Identifying the boundary conditions in GPSI was found to be of great significance. Compared to the standard ES-MDA method, the ES-MDA with a wheel battle strategy proposed here could improve the inversion accuracy, and had certain effectiveness and practicality.

PV Panel Model Parameter Estimation by Using Particle Swarm Optimization and Artificial Neural Network.

Photovoltaic (PV) panels are one of the popular green energy resources and PV panel parameter estimations are one of the popular research topics in PV panel technology. The PV panel parameters could be used for PV panel health monitoring and fault diagnosis. Recently, a PV panel parameters estimation method based in neural network and numerical current predictor methods has been developed. However, in order to further improve the estimation accuracies, a new approach of PV panel parameter estimation is proposed in this paper. The output current and voltage dynamic responses of a PV panel are measured, and the time series of the I-V vectors will be used as input to an artificial neural network (ANN)-based PV model parameter range classifier (MPRC). The MPRC is trained using an I-V dataset with large variations in PV model parameters. The results of MPRC are used to preset the initial particles' population for a particle swarm optimization (PSO) algorithm. The PSO algorithm is used to estimate the PV panel parameters and the results could be used for PV panel health monitoring and the derivation of maximum power point tracking (MMPT). Simulations results based on an experimental I-V dataset and an I-V dataset generated by simulation show that the proposed algorithms can achieve up to 3.5% accuracy and the speed of convergence was significantly improved as compared to a purely PSO approach.

Investigation on the damage model of rock subjected to freeze-thaw cycles considering its fracture voids compaction

The investigation on the mechanical properties and damage model of rock subjected to freeze-thaw cycles (FTCs) holds significant theoretical for assessing the deformation of rock mass engineering in cold regions. When the rocks subjected to FTCs, the fracture voids of rocks increase with increasing number of FTCs, and the rocks exhibit nonlinear deformation characteristics as its fracture voids compaction. Based on the dissipation energy evolution law and distribution characteristics of rocks under loading, the critical point of rock compaction deformation was determined. A constitutive model for rock subjected to FTCs considering its fracture voids compaction was established based on macroscopic and microscopic damage levels. The rationality of the model was validated using experimental results, and a comparison was made between the calculation results of proposed model and model that not consider fracture voids compaction. The evolution properties of rock damage variable and stress under different FTCs were analyzed. The variation of model parameters with the FTCs and the influence of model parameters on the deformation characteristics of rock was analyzed.

Synchronization of two non-identical Chialvo neurons

We investigate the synchronization between two neurons using the stochastic version of the map-based Chialvo model. To simulate non-identical neurons, a mismatch is introduced in one of the main parameters of the model. Subsequently, the synchronization of the neurons is studied as a function of this mismatch, the noise introduced in the stochastic model, and the coupling strength between the neurons. We propose the simplest neural network for study, as its analysis is more straightforward and does not compromise generality. Within this network, two non-identical neurons are electrically coupled. In order to understand whether specific behaviors affect the global behavior of the system, we consider different cases related to the behavior of the neurons (chaotic or periodic). Furthermore, we study how variations in model parameters affect the firing frequency in each case. Additionally, we consider that the two neurons have both excitatory and inhibitory couplings. Consequently, we identify critical values of noise and mismatch for achieving satisfactory synchronization between the neurons in each case. Finally, we propose that the results have general applicability across various neuron models.

Pharmacogenetic guided versus standard warfarin dosing for routine clinical care with its pharmacoeconomic impact: a randomized controlled clinical trial.

Empirical use of pharmacogenetic test(PGT) is advocated for many drugs, and resource-rich setting hospitals are using the same commonly. The clinical translation of pharmacogenetic tests in terms of cost and clinical utility is yet to be examined in hospitals of low middle income countries (LMICs). The present study assessed the clinical utility of PGT by comparing the pharmacogenetically(PGT) guided- versus standard of care(SOC)- warfarin therapy, including the health economics of the two warfarin therapies. An open-label, randomized, controlled clinical trial recruited warfarin-receiving patients in pharmacogenetically(PGT) guided- versus standard of care(SOC)- study arms. Pharmacogenetic analysis of CYP2C9*2(rs1799853), CYP2C9*3(rs1057910) and VKORC1(rs9923231) was performed for patients recruited to the PGT-guided arm. PT(Prothrombin Time)-INR(international normalized ratio) testing and dose titrations were allowed as per routine clinical practice. The primary endpoint was the percent time spent in the therapeutic INR range(TTR) during the 90-day observation period. Secondary endpoints were time to reach therapeutic INR(TRT), the proportion of adverse events, and economic comparison between two modes of therapy in a Markov model built for the commonest warfarin indication- atrial fibrillation. The study enrolled 168 patients, 84 in each arm. Per-protocol analysis showed a significantly high median time spent in therapeutic INR in the genotype-guided arm(42.85%; CI 21.4-66.75) as compared to the SOC arm(8.8%; CI 0-27.2)(p < 0.00001). The TRT was less in the PG-guided warfarin dosing group than the standard-of-care dosing warfarin group (17.85 vs. 33.92 days) (p = 0.002). Bleeding and thromboembolic events were similar in the two study groups. Lifetime expenditure was ₹1,26,830 in the PGT arm compared to ₹1,17,907 in the SOC arm. The QALY gain did not differ in the two groups(3.9 vs. 3.65). Compared to SOC, the incremental cost-utility ratio was ₹35,962 per QALY gain with PGT test opting. In deterministic and probabilistic sensitivity analysis, the base case results were found to be insensitive to the variation in model parameters. In the cost-effectiveness-acceptability curve analysis, a 90% probability of cost-effectiveness was reached at a willingness-to-pay(WTP) of ₹ 71,630 well below one time GDP threshold of WTP used. Clinical efficacy and the cost-effectiveness of the warfarin pharmacogenetic test suggest its routine use as a point of care investigation for patient care in LMICs.

CFD-DEM-based evaluation of main-channel sediment transport processes subject to supplement from a steep tributary

The accumulation of sediments within mountainous gullies serves as a significant source for the main channel. Understanding the transport characteristics is crucial for comprehending reach-scale sediment supply features and channel morphodynamics. This study addresses the previously unexplored challenges in accurately quantifying variations in sediment transport rates, particularly during critical transitions such as the mobilization of static sediment pulses into continuous motion. To accomplish this objective, a combined Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) computational framework is employed to directly simulate the detailed trajectories of all particles. After validating the CFD-DEM model, we examine the effects of three major influential factors (particle size, channel slope and channel width) on sediment transport characteristics. The results indicate that the sediment transport rate exhibits rapid rise to peak followed by gradual attenuation process, described by a long-tail effect. The cross-sectional averaged sediment transport rate positively correlates with the width of sediment transport belt. The downstream propagation of sediment is enhanced by steeper channel slope and narrower channel width. In addition, we find that cross-sectional averaged sediment transport rate controlled by these factors exhibits inherent self-similarity. The temporal variation of dimensionless sediment transport rate for a certain section can be modeled by a modified two-branch Weibull function encompasses three model parameters dependent on influential factors and longitudinal location. The parameter variation of the functional model, combined with linear variation of the three main influencing factors, provides support for explaining sediment transport processes in channels of dimensions different from those studied. These findings enhance the understanding of the formation and evolution mechanism of sediment transport induced by upstream loose deposit destruction.