Physics-based Model Research Articles

Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches

Climate change and complex anthropogenic activities have raised significant concerns regarding Precipitation-Runoff Relationships (PRR). Traditional methods, assuming stationary and linear conditions, often fail to adequately capture these intricate links. To address the limitations, we proposed an integrated framework, employing the Driving indices for Precipitation-Runoff relationships within the nonStationary and nonLinear theory approaches (DPRS and DPRL) to identify the possible driving mechanisms in PRR. The framework is validated across five sub-basins (WRB1-WRB5) within the Wei River Basin, known for its high spatiotemporal variability and intense anthropogenic activities. Spatiotemporal dynamics, nonstationary processes, and nonlinear interactions among various factors are assessed, including climate forcing, groundwater, vegetation dynamics, and anthropogenic influences. DPRS and DPRL assessments revealed that baseflow significantly influences PRR but with high uncertainty. Potential evapotranspiration plays a dominant role in driving negative PRR changes in WRB5 (weakening the correlation between precipitation and runoff), while vegetation dynamics negatively affect PRR with lower uncertainty. Anthropogenic influences represented by Impervious Surface Ratio (ISR), Night-Time Light (NTL), and population density (POP) exhibit varying driving levels, with ISR having the strongest and direct impact, closely linked to urbanization processes and scale within the study cases. The mutual validation of DPRS and DPRL confirms the dominance of baseflow in the Wei River Basin, with urbanization contributing to high ISR, NTL, and POP driving levels in WRB2 and WRB3. Afforestation policies intensify vegetation dynamics’ impact in WRB4 and WRB5. This framework extends its utility to analyze various land evapotranspiration and soil moisture content at different depths in the PRR, supported by a physically-based hydrological model. Basin complexity is further employed to validate the reliability of the assessment outcomes. These insights contribute to a more comprehensive understanding of hydrological processes and facilitate informed decisions for sustainable water resource management within the basin.

A multi-method approach for assessing groundwater vulnerability of shallow aquifers in the Marchfeld region (Austria)

Study regionMarchfeld region (Austria) Study focusA multi-method and multi-scale assessment of the intrinsic groundwater vulnerability to generic pollutants was carried out. At the regional scale, a parametric method, to assess the intrinsic groundwater vulnerability, and a transfer function model, to assess the travel time of a generic and non-reactive pollutant through the unsaturated zone, were applied. At the site-specific scale, the travel time of the peak concentration was evaluated by using a physically-based hydrological model. The comparison of results of different approaches allowed mutual validation and advanced the knowledge about the assessment of groundwater vulnerability. New hydrogeological insights for the regionTo assess the groundwater vulnerability, a detailed hydrogeological map of the study area was reconstructed. A large variability of hydrogeological, morphological and anthropic conditions was recognized. Alluvial aquifers formed by high-permeability deposits hosting shallow groundwater circulation are characterized by the highest groundwater vulnerability. Contrarily, lower groundwater vulnerability was recognized for aquifers formed by low-permeability deposits, favoring a reduction of infiltration processes and a major attenuation of pollutants’ potential effects. The presented multi-method approach revealed how comparing the results of a DRASTIC-like method and two process-based models can deliver hints regarding their suitability, different spatial densities and quality of required inputs, and effectiveness. Finally, the potential strong impact of some agricultural practices was confirmed.

Global sensitivity analysis of ultrasonic testing simulations of slot-like defects with multifidelity modeling

Abstract In this paper, an efficient global sensitivity analysis (GSA) method for simulation-based ultrasonic testing (UT) of slot-like defects using multifidelity modeling with novel termination criterion is proposed. GSA quantifies the effect of quantities of interest with variability (e.g., position, height, and angle) on the output (e.g., amplitude). GSA with Sobol' indices requires the use of Monte Carlo simulations (MCS) when dealing with nonlinear problems having many parameters. It is impractical to perform GSA directly on high-fidelity physics-based models due to their long evaluation times and the large number of required samples. Multifidelity methods construct surrogate models based on data from an accurate high-fidelity model (HFM) and fast low-fidelity models (LFMs). The multifidelity surrogates evaluate quickly and can be used in lieu of the HFM to accelerate the GSA. Conventional multifidelity methods construct the surrogate to meet a prespecified error metric before using it within an analysis. This requires a separate set of testing data and an often arbitrary error metric threshold. To avoid these, a novel multifidelity modeling termination criterion for GSA is proposed that is based on the absolute relative change of the Sobol' indices. The proposed approach is demonstrated on a simulated UT case inspecting a slot-like defect with three uncertainty variables. The results show a potential for significant reduction in computational cost compared with conventional approaches.

Physics-Based Practical Speckle Noise Modeling for Optical Coherence Tomography Image Denoising

Optical coherence tomography (OCT) has been extensively utilized in the field of biomedical imaging due to its non-invasive nature and its ability to provide high-resolution, in-depth imaging of biological tissues. However, the use of low-coherence light can lead to unintended interference phenomena within the sample, which inevitably introduces speckle noise into the imaging results. This type of noise often obscures key features in the image, thereby reducing the accuracy of medical diagnoses. Existing denoising algorithms, while removing noise, tend to also damage the structural details of the image, affecting the quality of diagnosis. To overcome this challenge, we have proposed a speckle noise (PSN) framework. The core of this framework is an innovative dual-module noise generator that can decompose the noise in OCT images into speckle noise and equipment noise, addressing each type independently. By integrating the physical properties of noise into the design of the noise generator and training it with unpaired data, we are able to synthesize realistic noise images that match clear images. These synthesized paired images are then used to train a denoiser to effectively denoise real OCT images. Our method has demonstrated its superiority in both private and public datasets, particularly in maintaining the integrity of the image structure. This study emphasizes the importance of considering the physical information of noise in denoising tasks, providing a new perspective and solution for enhancing OCT image denoising technology.

Modeling seasonal ice and its impact on the thermal regime of a shallow boreal lake using the Canadian small Lake model

At high latitudes, lake-atmosphere interactions are disrupted for several months of the year by the presence of an ice cover. By isolating the water column from the atmosphere, ice, typically topped by snow, drastically alters albedo, surface roughness, and heat exchanges relative to the open water period, with major climatic, ecological, and hydrological implications. Lake models used to simulate the appearance and disappearance of the ice cover have rarely been validated with detailed in situ observations of snow and ice. In this study, we investigate the ability of the physically-based 1D Canadian Small Lake Model (CSLM) to simulate the freeze-up, ice-cover growth, and breakup of a small boreal lake. The model, driven offline by local weather observations, is run on Lake Piché, 0.15 km2 and 4 m deep (47.32°N; 71.15°W) from 25 October 2019 to 20 July 2021, and compared to observations of the temperature profile and ice and snow cover properties. Our results show that the CSLM is able to reproduce the total ice thickness (average error of 15 cm) but not the ice type-specific thickness, underestimating clear ice and overestimating snow ice. CSLM manages to reproduce snow depth (errors less than 10 cm). However, it has an average cold bias of 2°C and an underestimation of average snow density of 34 kg m−3. Observed and model freeze-up and break-up dates are very similar, as the model is able to predict the longevity of the ice cover to within 2 weeks. CSLM successfully reproduces seasonal stratification, the mixed layer depth, and surface water temperatures, while it shows discrepancies in simulating bottom waters especially during the open water period.

A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering

The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems, this paper introduces a physics-based dimension reduction and modeling method. According to scientific principles, original features are transformed into causal variables with physical meaning. Then important causal variables are selected through regularization in machine learning. Finally, a concise prediction model with physical representation is established. An application to tunnel engineering demonstrates that the generated low-dimensional space reveals the essential laws between the control parameter and its main influencing factors (i.e., the geological conditions, operating status, and structural characteristics of the tunneling equipment), which can further provide a better prediction model balancing accuracy and dimensionality.

Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach

Uneven temperature distributions can significantly impact battery performance and cycle life. Industrial applications, in particular, where unknown disturbances and limited sensors exist, pose significant challenges for accurately estimating the battery temperature field. This work proposes a multi-source information fusion framework for capturing the spatiotemporal temperature dynamics of pouch-type Lithium-ion batteries. First, we develop a system model of the battery thermal process based on physical insights, considering unknown parameter deviations and unmodeled dynamics. Next, we use the Galerkin-spectral method to expand the spatiotemporal variable and reduce the original infinite-dimensional system to a low-order model containing the most important system modes. The unknown component of the model is then entirely stripped and integrated into a nonlinear term. To close the reality gap of the physics-based model, we subsequently develop an error compensation model that learns the dynamic behavior from sparse observations. Ultimately, our framework design yields a fusion-driven model for reliable temperature field prediction. Simulations and experimental data validate the superior performance and generalization capability of the proposed method.

A physics-based model of thermodynamically varying fuel moisture content for fire behavior prediction

Fuel moisture content (FMC) is a critical parameter in fire and plume behaviors, showing diurnal and spatial variations influenced by local meteorological conditions, soil characteristics, and fuel properties. In low-intensity fires, small-scale FMC variations intensify, leading to an amplification of their effects on fire physics. In an effort to capture these variations, this paper presents the development of a physics-based model that couples a thermodynamic-based FMC prediction model for dead fuels with the Fire Dynamics Simulator of the National Institute of Standards and Technology. The model accuracy is validated against several existing experimental data, showing improvements over the baseline model which uses the kinetic-based Arrhenius drying approach. A case study of flame propagation in a small fuel bed is also presented, indicating the improved performance of the new model and its novel capabilities in capturing complex processes of fuel drying and moisture flux exchanges between the fuel and ambient atmosphere.

Development of machine learning based model for low-temperature PEM fuel cells

Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R2≥ 0.99 and MAPE ≤ 0.06 during testing. These models are further validated on experimental data with R2≥ 0.90 and MAPE ≤ 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.

Rapid Modeling of Photopolymerization in Projection Two-Photon Lithography via an Operator Splitting Finite Difference Method

Abstract Two-photon lithography (TPL) is an attractive technique for nanoscale additive manufacturing of functional 3D structures due to its ability to print sub-diffraction features with light. Despite its advantages, it has not been widely adopted due to its slow point-by-point writing mechanism. Projection TPL (P-TPL) is a high-throughput variant that overcomes this limitation by enabling the printing of entire 2D layers at once. However, printing the desired 3D structures is challenging due to the lack of fast and accurate process models. Here, we present a fast and accurate physics-based model of P-TPL to predict the printed geometry and the degree of curing. Our model implements a finite difference method enabled by operator splitting to solve the reaction-diffusion rate equations that govern photopolymerization. When compared with finite element simulations, our model is at least a hundred times faster and its predictions lie within 5% of the predictions of the finite element simulations. This rapid modeling capability enabled performing high-fidelity simulations of printing of arbitrarily complex 3D structures for the first time. We demonstrate how these 3D simulations can predict those aspects of the 3D printing behavior that cannot be captured by simulating the printing of individual 2D layers. Thus, our models provide a resource-efficient and knowledge-based predictive capability that can significantly reduce the need for guesswork-based iterations during process planning and optimization.

Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative

Physics-based modeling methods have the potential to investigate the mechanical factors associated with knee osteoarthritis (OA) and predict the future radiographic condition of the joint. However, it remains unclear what level of detail is optimal in these methods to achieve accurate prediction results in cohort studies. In this work, we extended a template-based finite element (FE) method to include the lateral and medial compartments of the tibiofemoral joint and simulated the mechanical responses of 97 knees under three conditions of gait loading. Furthermore, the effects of variations in cartilage thickness and failure equation on predicted cartilage degeneration were investigated. Our results showed that using neural network-based estimations of peak knee loading provided classification performances of 0.70 (AUC, p < 0.05) in distinguishing between knees that developed severe OA or mild OA and knees that did not develop OA eight years after a healthy radiographic baseline. However, FE models incorporating subject-specific femoral and tibial cartilage thickness did not improve this classification performance, suggesting there exists an optimal point between personalized loading and geometry for discrimination purposes. In summary, we proposed a modeling framework that streamlines the rapid generation of individualized knee models achieving promising classification performance while avoiding motion capture and cartilage image segmentation.

Development of a multi-layer green-ampt infiltration based modeling framework for hydrologic process simulation and design of various low impact development measures for mitigating urban flooding

ABSTRACT Low Impact Development (LID) measures have proven to be effective in reducing the runoff volume and the peak of runoff at small spatial scales. Assessment of the hydrological impacts of LID measures at river basin scales is currently limited by the absence of adequate physically-based process models that can simulate LID processes at such scales. In this study, an attempt has been made to develop LID modules to simulate the water balance and hydrological processes based on the Multi-Layer Green-Ampt (MLGA) infiltration method, which is an advancement to the existing LID modeling techniques. The performance of the MLGA-based LID modules was benchmarked with the process-intensive numerical tool HYDRUS 1D for about 5000 soil samples across the soil textural classes. The results indicated that the MLGA-based model simulates the hydrological processes in close agreement with HYDRUS for soils with moderate-to-high hydraulic conductivity values, which are amenable for LID implementation.

Study on the influence of κ-carbide on the high temperature flow behavior of the medium-Mn lightweight steel: Modeling and characterization

Isothermal compression tests were carried out on the Fe-11Mn-10Al-0.9C medium-Mn lightweight steel in the temperature range from 800 °C to 1100 °C and strain rate range from 0.001 s−1 to 10 s−1 to investigate the influence of κ-carbide on the hot flow behavior. The true stress-true strain curves were analyzed to evaluate the role of κ-carbide on the softening phenomena during straining. It's found flow stress drops more remarkably from the peak point to the end of deformation at low temperature and low strain rate, that condition is favorable for the precipitation of κ-carbide instead of the occurrence of DRX. The activation energy of hot deformation based on the peak stress was calculated as 454.517 kJ mol−1, but this typical strain-compensated Arrhenius-type constitutive equation was found inappropriate to model the flow behavior of the present steel, especially at the low temperature regime. By comparison, a physically-based constitutive model consisting of Bergström's equation (ε<εp) and Kolmogorov-Johnson-Mehl-Avrami's equation (ε>εp) was developed and could predict the flow behavior of the steel accurately. Electron backscattered diffraction (EBSD) analysis revealed the formation of necklace structure at high strain rate and the dynamic recrystallization (DRX) proceeded more adequately with the decrease in strain rate and increase in the deformation temperature. The precipitation of κ-carbides in both austenite grain interior and boundaries (GI and GBs) were enhanced at relatively low deformation temperature (e.g., 800 °C) and greatly improved the peak stress by hindering the dislocation motion. With the increase of deformation, the GB κ-carbide gradually coarsened and induced the decomposition of deformed austenite via the eutectoid reaction (γ→α+κ) at lower strain rate (e.g., 0.001 s−1) due to the sufficient transformation time, consequently leading to the remarkable flow stress softening. The microstructure characterization result was in agreement with variations of parameters (i.e., U, Ω, ρp and nA) with different deformation condition.

Modeling Mechanochemical Depolymerization of PET in Ball-Mill Reactors Using DEM Simulations.

Developing efficient and sustainable chemical recycling pathways for consumer plastics is critical for mitigating the negative environmental implications associated with their end-of-life management. Mechanochemical depolymerization reactions have recently garnered great attention, as they are recognized as a promising solution for solvent-free transformation of polymers to monomers in the solid state. To this end, physics-based models that accurately describe the phenomena within ball mills are necessary to facilitate the exploration of operating conditions that would lead to optimal performance. Motivated by this, in this paper we develop a mathematical model that couples results from discrete element method (DEM) simulations and experiments to study mechanically-induced depolymerization. The DEM model was calibrated and validated via video experimental data and computer vision algorithms. A systematic study on the influence of the ball-mill operating parameters revealed a direct relationship between the operating conditions of the vibrating milling vessel and the total energy supplied to the system. Moreover, we propose a linear correlation between the high-fidelity DEM simulation results and experimental monomer yield data for poly(ethylene terephthalate) depolymerization, linking mechanical and energetic variables. Finally, we train a reduced-order model to address the high computational cost associated with DEM simulations. The predicted working variables are used as inputs to the proposed mathematical expression which allows for the fast estimation of monomer yields.

Shear force effect of the dry process on cathode contact coverage in all-solid-state batteries

The state-of-the-art all-solid-state batteries have emerged as an alternative to the traditional flammable lithium-ion batteries, offering higher energy density and safety. Nevertheless, insufficient intimate contact at electrode-electrolyte surface limits their stability and electrochemical performance, hindering the commercialization of all-solid-state batteries. Herein, we conduct a systematic investigation into the effects of shear force in the dry electrode process by comparing binder-free hand-mixed pellets, wet-processed electrodes, and dry-processed electrodes. Through digitally processed images, we quantify a critical factor, ‘coverage’, the percentage of electrolyte-covered surface area of the active materials. The coverage of dry electrodes was significantly higher (67.2%) than those of pellets (30.6%) and wet electrodes (33.3%), enabling superior rate capability and cyclability. A physics-based electrochemical model highlights the effects of solid diffusion by elucidating the impact of coverage on active material utilization under various current densities. These results underscore the pivotal role of the electrode fabrication process, with the focus on the critical factor of coverage.

Computational Method for Optimal Electrolyte Screening Using Bayesian Optimization and Physics Based Battery Model

Lithium-ion batteries (LIBs) powering electric vehicles and large-scale energy storage depend significantly on the composition of liquid electrolyte for optimal performance. We propose a framework coupling Bayesian optimization and physics based battery models to identify electrolytes optimal for specific set of requirements such as less capacity fade and internal heating etc. Our approach is validated through multiple case studies, demonstrating the framework’s efficacy in optimizing electrolyte properties. Additionally, we introduce a deviation index to quantify the proximity of the optimal electrolyte to those in a predefined database. With adaptability to various LIB metrics and battery chemistries, it provides a systematic and efficient approach for screening electrolytes based on system-level performance using physics-based models, contributing to advancements in battery technology for sustainable energy storage systems.

A Physics-based Model Assisted by Machine-Learning for Sodium-ion Batteries with both Liquid and Solid Electrolytes

In the absence of experimental data of fully developed hierarchical 3D sodium solid-state batteries, we developed an improved continuum model by relying on Machine Learning-assisted parameter fitting to uncover the intrinsic material properties that can be transferred into different battery models. The electrochemical system simulated has sodium metal P2-type Na2/3[Ni1/3Fe1/12Mn7/12]O2 (NNFMO) as the cathode material, paired with two types of electrolytes viz, the organic liquid electrolyte and a solid polymer electrolyte. We implemented a 1D continuum model in COMSOL to suit both liquid and solid electrolytes, then used a Gaussian Process Regressor to fit and evaluate the electrochemical parameters in both battery systems. To enhance the generalizability of our model, the liquid cell and solid cell models share the same OCV input for the cathode materials. The resulting parameters are well aligned with their physical meaning and literature values. The continuum model is then used to understand the effect of increasing the thickness of the cathode and current density by analyzing the cathode utilization, and the overpotentials arising from transport and charge transfer. This 1D model and the parameter set are ready to be used in a 3D battery architecture design.

Numerical simulation of microstructural failure of natural fibre–hydrogel composites

In this article, we conduct a numerical simulation to investigate the failure mechanism of natural fibre–hydrogel composites under tensile deformation and swelling. Firstly, a physical-based constitutive model is chosen to describe the deformation of the hydrogel matrix. Concurrently, a macroscopic phenomenological hyperelastic constitutive model, incorporated into the material database of Abaqus, is employed to describe the mechanical responses of natural fibres. Then, we implement the model for a numerical analysis. The material parameters of the model for the hydrogel matrix and fibres are determined by a trial-and-error method from the experimental curves of pure hydrogel and fibres; the numerically simulated stress–strain curve of the composites is compared with the experimental one to verify the effectiveness of the simulation. Subsequently, several numerical analyses are performed to discuss the failure mechanism of hydrogel composites caused by the microstructural features of fibres. The findings revealed that fibre protrusion, fibre orientation deviation, and interface strength significantly affect the local failure of the composites during tensile deformation and swelling; the composites’ strength and toughness mainly depend on the cooperation of various microstructural features. These results provide valuable insights into the design of fibre–hydrogel composites.

A novel physics-based modeling approach for tangential fretting contact behavior of jointed interface considering multi-scale effects

A novel physics-based constitutive model has been developed to map the tangential fretting behavior of joint surfaces. The model integrates the fractal normal contact model, which considers multi-scale effects, and the Iwan model through the Coulomb friction law. In this model, a new distribution of yield force is proposed for Jenkins elements, which is determined by fractal topography and material parameters and related to the scale of asperity. The effects of fractal topography, material parameters, and the normal load applied to the joint surface on the tangential responses such as tangential force, tangential stiffness, energy dissipation, and the distribution of yield force have been discussed. It has been found that the fractal parameters D and G have opposite effects on the tangential responses and yield force distribution.

Capacitance modeling, simulation and RF characterization of horizontal floating gate field effect transistor (H-FGFET) for gas sensing application

In this paper, physics-based capacitance model has been developed for horizontal floating gate field effect transistor (H-FGFET). The horizontal design of floating gate (FG) FET allows detection of large gas molecules like nitrogen dioxide (NO2), sulphur dioxide (SO2), hydrogen sulfide (H2S), carbon dioxide (CO2) etc. In order to validate the proposed model, the device has been designed using Sentaurus TCAD simulator and results have been validated with experimental data. Different electrical parameters such as transfer characteristics, switching ratio, output characteristics, threshold voltage, drain induced barrier lowering (DIBL), C-V characteristics, transconductance, output conductance and RF characteristics of the device have been analyzed at room temperature. The simulation result shows that increase in channel length of H-FGFET results in decreased leakage current, improved switching performance, increased gate capacitance and reduced DIBL. The comparative analysis of H-FGFET with previously reported floating gate FET structures reveal that proposed device offers highest switching ratio. The RF characteristics of H-FGFET illustrates that proposed device can operate efficiently as an amplifier for the frequency range of 0.1 GHz to 100 GHz. Further, the proposed device has also been tested for the detection of nitrogen dioxide gas and results reveal that with increase in operating temperature from 30 °C to 180 °C the leakage current of the device increases from 10−13 to 10−9 and OFF current sensitivity of the proposed sensor changes by 82% on changing the work function of sensing layer from 50 meV to 250 meV.